Instantaneous network model diagnostics - unbalanced statistics

This file shows diagnostics for instantaneous network models fit using unbalanced racial/ethnic mixing matrices and degree terms as reported by egos. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.

Load packages and model fits

rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")
## Loading required package: lattice
## Loading required package: RColorBrewer
library("knitr")
library("kableExtra")

load(file = "/homes/dpwhite/R/GitHub Repos/WHAMP/Model fits and simulations/Fit tests and debugging/est/fit.i.buildup.unbal.rda")

Model terms and control settings

Model terms and target statistics
Terms Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
edges 478.5 478.5 478.5 478.5 478.5 478.5 478.5 478.5
nodefactor.deg.main.deg.pers.0.1 NA NA NA 173.2 173.2 173.2 173.2 173.2
nodefactor.deg.main.deg.pers.0.2 NA NA NA 37.2 37.2 37.2 37.2 37.2
nodefactor.deg.main.deg.pers.1.0 NA NA NA 36.6 36.6 36.6 36.6 36.6
nodefactor.deg.main.deg.pers.1.1 NA NA NA 135.4 135.4 135.4 135.4 135.4
nodefactor.deg.main.deg.pers.1.2 NA NA NA 145.9 145.9 145.9 145.9 145.9
nodefactor.riskg.O2 NA NA NA NA NA NA 0.4 0.4
nodefactor.riskg.O3 NA NA NA NA NA NA 6.9 6.9
nodefactor.riskg.O4 NA NA NA NA NA NA 109.5 109.5
nodefactor.riskg.Y1 NA NA NA NA NA NA 1.3 1.3
nodefactor.riskg.Y2 NA NA NA NA NA NA 8.2 8.2
nodefactor.riskg.Y3 NA NA NA NA NA NA 70.8 70.8
nodefactor.riskg.Y4 NA NA NA NA NA NA 762.0 762.0
nodefactor.race..wa.B NA 75.2 75.2 75.2 75.2 75.2 75.2 75.2
nodefactor.race..wa.H NA 100.8 100.8 100.8 100.8 100.8 100.8 100.8
nodefactor.region.EW NA NA NA NA 83.4 83.4 83.4 83.4
nodefactor.region.OW NA NA NA NA 242.2 242.2 242.2 242.2
nodematch.race..wa.B NA NA 2.5 2.5 2.5 2.5 2.5 2.5
nodematch.race..wa.H NA NA 13.3 13.3 13.3 13.3 13.3 13.3
nodematch.race..wa.O NA NA 286.9 286.9 286.9 286.9 286.9 286.9
nodematch.region NA NA NA NA NA NA NA 382.8
absdiff.sqrt.age NA NA NA NA NA 380.0 380.0 380.0
nodematch.role.class.I -Inf -Inf -Inf -Inf -Inf -Inf -Inf -Inf
nodematch.role.class.R -Inf -Inf -Inf -Inf -Inf -Inf -Inf -Inf

The control settings for these models are:

set.control.ergm = control.ergm(MCMC.interval = 1e+5,
                                MCMC.samplesize = 7500,
                                MCMC.burnin = 1e+6,
                                MPLE.max.dyad.types = 1e+7,
                                MCMLE.maxit = 400,
                                parallel = np/2, 
                                parallel.type="PSOCK"))

MCMC diagnostics

Model 1

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##           Mean             SD       Naive SE Time-series SE 
##         0.3720        21.8648         0.1262         0.1276 
## 
## 2. Quantiles for each variable:
## 
##     2.5%      25%      50%      75%    97.5% 
## -42.5122 -14.5122   0.4878  15.4878  43.4878 
## 
## 
## Sample statistics cross-correlations:
##       edges
## edges     1
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.016018241
## Lag 2e+05  0.004478845
## Lag 3e+05 -0.021908757
## Lag 4e+05  0.004070781
## Lag 5e+05 -0.014951207
## Chain 2 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.013984710
## Lag 2e+05  0.019331419
## Lag 3e+05 -0.030323245
## Lag 4e+05  0.043858126
## Lag 5e+05 -0.001033153
## Chain 3 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.003639058
## Lag 2e+05  0.004865561
## Lag 3e+05 -0.014513719
## Lag 4e+05  0.033034544
## Lag 5e+05  0.013242954
## Chain 4 
##                   edges
## Lag 0      1.0000000000
## Lag 1e+05 -0.0113589055
## Lag 2e+05  0.0028440719
## Lag 3e+05 -0.0033111683
## Lag 4e+05  0.0003207483
## Lag 5e+05  0.0006422744
## Chain 5 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.008253553
## Lag 2e+05  0.012824488
## Lag 3e+05  0.022777469
## Lag 4e+05 -0.002275063
## Lag 5e+05 -0.013043688
## Chain 6 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.009619496
## Lag 2e+05  0.040609295
## Lag 3e+05  0.031020583
## Lag 4e+05  0.006720460
## Lag 5e+05 -0.008003514
## Chain 7 
##                   edges
## Lag 0      1.0000000000
## Lag 1e+05 -0.0143819029
## Lag 2e+05 -0.0001361824
## Lag 3e+05 -0.0026294537
## Lag 4e+05  0.0295354653
## Lag 5e+05 -0.0011827580
## Chain 8 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.023899635
## Lag 2e+05  0.002757309
## Lag 3e+05  0.014959656
## Lag 4e+05 -0.013742463
## Lag 5e+05  0.023638649
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.6633 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.5071488 
## Joint P-value (lower = worse):  0.5203464 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## -0.971 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.3315318 
## Joint P-value (lower = worse):  0.3349909 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.8039 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.4214728 
## Joint P-value (lower = worse):  0.4318732 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.3837 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.7011725 
## Joint P-value (lower = worse):  0.7207989 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
## edges 
## 1.379 
## 
## Individual P-values (lower = worse):
##    edges 
## 0.167823 
## Joint P-value (lower = worse):  0.1682671 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
## edges 
## 1.207 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.2273946 
## Joint P-value (lower = worse):  0.3840555 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.1294 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.8970132 
## Joint P-value (lower = worse):  0.890929 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## -1.146 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.2519562 
## Joint P-value (lower = worse):  0.261183 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 2

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                         Mean    SD Naive SE Time-series SE
## edges                 0.9614 21.88  0.12632        0.12731
## nodefactor.race..wa.B 0.2007  9.00  0.05196        0.05269
## nodefactor.race..wa.H 0.5336 10.59  0.06114        0.06185
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%     50%    75% 97.5%
## edges                 -41.51 -13.512  0.4878 15.488 44.49
## nodefactor.race..wa.B -17.19  -6.186 -0.1865  5.814 17.81
## nodefactor.race..wa.H -19.84  -6.835  0.1647  7.165 22.16
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.race..wa.B
## edges                 1.0000000            0.39130141
## nodefactor.race..wa.B 0.3913014            1.00000000
## nodefactor.race..wa.H 0.4271832            0.09322341
##                       nodefactor.race..wa.H
## edges                            0.42718321
## nodefactor.race..wa.B            0.09322341
## nodefactor.race..wa.H            1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0     1.000000000           1.000000000           1.000000000
## Lag 1e+05 0.020924539          -0.001728139           0.042471861
## Lag 2e+05 0.004657611           0.020599080           0.011588301
## Lag 3e+05 0.025309430          -0.001020049           0.003335766
## Lag 4e+05 0.001809844           0.012411804           0.002872256
## Lag 5e+05 0.010043244           0.009925257          -0.015583031
## Chain 2 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.005990263          0.0324931877          -0.021898796
## Lag 2e+05 -0.004393225         -0.0108195034          -0.011794768
## Lag 3e+05  0.013725164         -0.0207867137           0.007152037
## Lag 4e+05 -0.019643857         -0.0004449611          -0.029858917
## Lag 5e+05 -0.003436412          0.0118199122           0.021592296
## Chain 3 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000            1.00000000
## Lag 1e+05 -0.016831117           0.020028798           -0.02494689
## Lag 2e+05  0.006467388          -0.010391921           -0.01439160
## Lag 3e+05  0.018090986           0.036099575           -0.01374998
## Lag 4e+05  0.002675135           0.002341193           -0.01142790
## Lag 5e+05  0.043779205          -0.023044599            0.02792746
## Chain 4 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.00000000          1.000000e+00           1.000000000
## Lag 1e+05  0.01188607          7.406158e-03          -0.008817549
## Lag 2e+05  0.01182311          1.970443e-02           0.044429840
## Lag 3e+05 -0.01168962         -1.790854e-03          -0.007306519
## Lag 4e+05  0.00638484          8.630448e-05          -0.001451798
## Lag 5e+05  0.01913623          2.178303e-02          -0.005134404
## Chain 5 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.014777566           0.014141124           0.001062696
## Lag 2e+05  0.004846138           0.015698034           0.004022546
## Lag 3e+05 -0.002658842          -0.002430491          -0.014419914
## Lag 4e+05  0.015942513           0.022601804           0.009566082
## Lag 5e+05 -0.002040663          -0.011697905           0.004148877
## Chain 6 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.0159125717          -0.015056973          0.0187918963
## Lag 2e+05  0.0165963665           0.013014869         -0.0161865146
## Lag 3e+05  0.0076073123           0.008702703          0.0059090585
## Lag 4e+05  0.0075842468          -0.004231786          0.0002864374
## Lag 5e+05 -0.0004512533           0.021194065          0.0045462134
## Chain 7 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.016913346          -0.007530441           0.035930846
## Lag 2e+05  0.012270322           0.027091974           0.023234930
## Lag 3e+05  0.008185145           0.004328202          -0.006993389
## Lag 4e+05 -0.007786605          -0.040473450          -0.002667202
## Lag 5e+05  0.046634577           0.010396538          -0.006305833
## Chain 8 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.022422046          -0.008454623           0.021156298
## Lag 2e+05  0.003685671           0.028369140           0.002074361
## Lag 3e+05 -0.000237236          -0.023406074          -0.003253354
## Lag 4e+05 -0.033875577           0.007127537           0.002089118
## Lag 5e+05  0.011989064           0.040213595           0.016461319
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             -1.346369              0.109260              0.004333 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.1781834             0.9129959             0.9965425 
## Joint P-value (lower = worse):  0.4138388 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                2.6311                3.6485                0.8415 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##           0.008510509           0.000263736           0.400040999 
## Joint P-value (lower = worse):  0.00154964 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               0.98471               0.08639              -1.07714 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.3247675             0.9311566             0.2814191 
## Joint P-value (lower = worse):  0.2950909 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.6880                0.1541                0.2131 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4914245             0.8775562             0.8312774 
## Joint P-value (lower = worse):  0.9048868 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.8053                0.9113                0.2640 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4206751             0.3621394             0.7917578 
## Joint P-value (lower = worse):  0.7717614 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.0622                0.4474                1.4001 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.2881595             0.6545878             0.1614761 
## Joint P-value (lower = worse):  0.535964 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -2.1597               -0.2486               -1.6979 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.03079316            0.80368434            0.08953553 
## Joint P-value (lower = worse):  0.1206014 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.4026               -1.0203                0.2226 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.6872617             0.3075832             0.8238166 
## Joint P-value (lower = worse):  0.6335743 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 3

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                         Mean     SD Naive SE Time-series SE
## edges                 -5.697 21.723  0.12542        0.12560
## nodefactor.race..wa.B  5.746  9.038  0.05218        0.05261
## nodefactor.race..wa.H  5.884 11.052  0.06381        0.06369
## nodematch.race..wa.B  -2.538  0.000  0.00000        0.00000
## nodematch.race..wa.H  -5.777  2.737  0.01580        0.01575
## nodematch.race..wa.O   5.776 17.112  0.09880        0.09919
## 
## 2. Quantiles for each variable:
## 
##                          2.5%      25%    50%    75%  97.5%
## edges                 -48.512 -20.5122 -5.512  8.488 37.488
## nodefactor.race..wa.B -11.186  -0.1865  5.814 11.814 23.814
## nodefactor.race..wa.H -14.835  -1.8353  6.165 13.165 28.165
## nodematch.race..wa.B   -2.538  -2.5375 -2.538 -2.538 -2.538
## nodematch.race..wa.H  -10.275  -7.2750 -6.275 -4.275 -0.275
## nodematch.race..wa.O  -26.884  -5.8841  5.116 17.116 39.116
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                           edges nodefactor.race..wa.B
## edges                 1.0000000           0.412984215
## nodefactor.race..wa.B 0.4129842           1.000000000
## nodefactor.race..wa.H 0.4414689          -0.004569947
## nodematch.race..wa.B         NA                    NA
## nodematch.race..wa.H  0.1226119          -0.002821983
## nodematch.race..wa.O  0.7858353          -0.001423039
##                       nodefactor.race..wa.H nodematch.race..wa.B
## edges                           0.441468921                   NA
## nodefactor.race..wa.B          -0.004569947                   NA
## nodefactor.race..wa.H           1.000000000                   NA
## nodematch.race..wa.B                     NA                    1
## nodematch.race..wa.H            0.496998811                   NA
## nodematch.race..wa.O           -0.003505582                   NA
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                          0.122611852          0.785835275
## nodefactor.race..wa.B         -0.002821983         -0.001423039
## nodefactor.race..wa.H          0.496998811         -0.003505582
## nodematch.race..wa.B                    NA                   NA
## nodematch.race..wa.H           1.000000000         -0.003893702
## nodematch.race..wa.O          -0.003893702          1.000000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05  0.0086966198           0.000235780           0.043097828
## Lag 2e+05  0.0004537199          -0.023071414          -0.024383476
## Lag 3e+05  0.0009233485          -0.007093079          -0.028156226
## Lag 4e+05 -0.0223691018           0.001701941           0.006689933
## Lag 5e+05  0.0378771843          -0.029648493           0.011797626
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.000652547          0.004782351
## Lag 2e+05                  NaN         -0.034080494          0.019086739
## Lag 3e+05                  NaN         -0.014957353          0.011821590
## Lag 4e+05                  NaN         -0.003393689         -0.020073926
## Lag 5e+05                  NaN          0.005775433          0.016523175
## Chain 2 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.002572814          -0.027001837          -0.022026524
## Lag 2e+05  0.006107758          -0.002675363          -0.015853305
## Lag 3e+05 -0.017325991          -0.001056323          -0.003327586
## Lag 4e+05 -0.004377187           0.028144003          -0.016236346
## Lag 5e+05  0.013241851          -0.001157907          -0.009856191
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.006326331          0.009362979
## Lag 2e+05                  NaN         -0.001377208          0.003930569
## Lag 3e+05                  NaN         -0.026697610         -0.025889399
## Lag 4e+05                  NaN         -0.034348755         -0.006927948
## Lag 5e+05                  NaN         -0.018898894          0.008728606
## Chain 3 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.00000000           1.000000000           1.000000000
## Lag 1e+05 -0.01342858          -0.008169847          -0.014948317
## Lag 2e+05 -0.01866900           0.022765793          -0.035228080
## Lag 3e+05  0.01792369           0.049297066           0.005043247
## Lag 4e+05 -0.02086499          -0.014059883          -0.012144445
## Lag 5e+05  0.01625335           0.010508217           0.010895722
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000         1.0000000000
## Lag 1e+05                  NaN          0.006570180        -0.0072212197
## Lag 2e+05                  NaN          0.019501574        -0.0073997329
## Lag 3e+05                  NaN          0.006574234         0.0056657757
## Lag 4e+05                  NaN         -0.002996293        -0.0117000140
## Lag 5e+05                  NaN          0.015145857        -0.0005154477
## Chain 4 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.005780135           0.005533543          -0.001029785
## Lag 2e+05  0.002000047          -0.008502600          -0.012204197
## Lag 3e+05  0.009920283           0.036455265           0.019654205
## Lag 4e+05 -0.014911219           0.012409217          -0.031390364
## Lag 5e+05 -0.021396208           0.005712549          -0.001318277
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN         1.0000000000          1.000000000
## Lag 1e+05                  NaN         0.0084489114         -0.003861306
## Lag 2e+05                  NaN         0.0007888953          0.002553965
## Lag 3e+05                  NaN        -0.0061040750          0.002327480
## Lag 4e+05                  NaN        -0.0255807443          0.007355392
## Lag 5e+05                  NaN         0.0321118075         -0.022250554
## Chain 5 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.018202872          0.0001218513          -0.005655794
## Lag 2e+05 -0.007556006          0.0277665833           0.021117064
## Lag 3e+05 -0.001006759          0.0241146429          -0.008363072
## Lag 4e+05  0.008570940          0.0121956376           0.015176457
## Lag 5e+05  0.006273073         -0.0178560047          -0.012413543
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.037590637         -0.007670929
## Lag 2e+05                  NaN          0.008528925         -0.005019357
## Lag 3e+05                  NaN          0.009187140         -0.006999959
## Lag 4e+05                  NaN          0.009879336         -0.005015392
## Lag 5e+05                  NaN          0.021124766          0.027970894
## Chain 6 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.013507135          -0.010167929          -0.021319865
## Lag 2e+05  0.028370763          -0.004472883           0.018682094
## Lag 3e+05  0.001552200          -0.028760819          -0.019678276
## Lag 4e+05 -0.007619653           0.019090574           0.002325194
## Lag 5e+05 -0.011084018          -0.013195103          -0.009016884
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN         1.000000e+00         1.0000000000
## Lag 1e+05                  NaN        -2.389309e-05        -0.0001420207
## Lag 2e+05                  NaN        -1.679521e-02         0.0212276394
## Lag 3e+05                  NaN         1.507757e-02         0.0027330858
## Lag 4e+05                  NaN         1.434195e-02        -0.0196526545
## Lag 5e+05                  NaN         7.754742e-03         0.0073500373
## Chain 7 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05 -0.0048343925           0.007318636           0.013655632
## Lag 2e+05  0.0405525089           0.002392997           0.038660708
## Lag 3e+05 -0.0079534448          -0.012064612           0.018916668
## Lag 4e+05  0.0004641492          -0.009410154          -0.026187897
## Lag 5e+05 -0.0094990065           0.009187710           0.007432344
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN         1.0000000000          1.000000000
## Lag 1e+05                  NaN         0.0294935696         -0.007903393
## Lag 2e+05                  NaN         0.0296890374          0.039016007
## Lag 3e+05                  NaN        -0.0007543228         -0.002613443
## Lag 4e+05                  NaN        -0.0091877863          0.017140804
## Lag 5e+05                  NaN         0.0062521557         -0.022640137
## Chain 8 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.026535275           0.023963337         -0.0215778185
## Lag 2e+05 -0.013408855           0.002719366         -0.0004377148
## Lag 3e+05  0.018208741           0.001112258          0.0118847408
## Lag 4e+05 -0.001386932           0.002671070          0.0101711571
## Lag 5e+05 -0.014070527          -0.001869706         -0.0188477269
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000         1.000000e+00
## Lag 1e+05                  NaN          0.012877744        -1.545197e-05
## Lag 2e+05                  NaN          0.007486491         1.240577e-02
## Lag 3e+05                  NaN          0.003659438        -9.756031e-03
## Lag 4e+05                  NaN         -0.031324766        -6.141203e-03
## Lag 5e+05                  NaN          0.031213351         2.973514e-03
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.6017               -0.8276                0.4741 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN                1.4902               -0.4053 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.5473409             0.4079075             0.6354477 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN             0.1361719             0.6852263 
## Joint P-value (lower = worse):  0.6448022 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.8193                0.3159                0.4620 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN                0.6071                0.7559 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4126217             0.7520466             0.6440820 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN             0.5437584             0.4497089 
## Joint P-value (lower = worse):  0.9656356 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.3489                0.1086               -1.7112 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN                0.4651                1.5527 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.72716135            0.91351094            0.08703814 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.64185140            0.12049224 
## Joint P-value (lower = worse):  0.1714478 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.7913                0.2382               -1.1382 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN                1.2727               -0.2143 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4287827             0.8117570             0.2550202 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN             0.2031182             0.8303179 
## Joint P-value (lower = worse):  0.3518503 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               0.07438              -0.98794              -0.41489 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN               0.43932               0.94797 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.9407053             0.3231832             0.6782210 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN             0.6604280             0.3431451 
## Joint P-value (lower = worse):  0.7327236 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -1.0925               -1.4804                0.6393 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN               -1.6687               -1.2655 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.27461738            0.13877829            0.52261733 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.09516917            0.20568379 
## Joint P-value (lower = worse):  0.1229187 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.9656                0.2359                0.5926 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN               -0.5475               -1.8293 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.33425109            0.81353292            0.55343486 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.58406565            0.06736037 
## Joint P-value (lower = worse):  0.4728179 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                2.0542                0.7466                1.2969 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN                1.0037                1.4942 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.03995882            0.45532357            0.19465634 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.31551480            0.13511693 
## Joint P-value (lower = worse):  0.5117922 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 4

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                                      Mean     SD Naive SE Time-series SE
## edges                            -5.88693 21.646  0.12497        0.12558
## nodefactor.deg.main.deg.pers.0.1  0.06243 14.296  0.08254        0.08222
## nodefactor.deg.main.deg.pers.0.2  0.12793  6.236  0.03600        0.03616
## nodefactor.deg.main.deg.pers.1.0 -0.04241  6.134  0.03541        0.03659
## nodefactor.deg.main.deg.pers.1.1 -0.04455 12.434  0.07179        0.07319
## nodefactor.deg.main.deg.pers.1.2 -0.13277 12.979  0.07494        0.07487
## nodefactor.race..wa.B             5.92377  8.983  0.05186        0.05190
## nodefactor.race..wa.H             5.37685 10.990  0.06345        0.06306
## nodematch.race..wa.B             -2.53754  0.000  0.00000        0.00000
## nodematch.race..wa.H             -5.83086  2.739  0.01582        0.01580
## nodematch.race..wa.O              5.86285 16.989  0.09808        0.09848
## 
## 2. Quantiles for each variable:
## 
##                                     2.5%      25%     50%    75%  97.5%
## edges                            -47.512 -20.5122 -6.5122  8.488 36.488
## nodefactor.deg.main.deg.pers.0.1 -27.175 -10.1754 -0.1754  9.825 28.825
## nodefactor.deg.main.deg.pers.0.2 -11.217  -4.2169 -0.2169  3.783 12.783
## nodefactor.deg.main.deg.pers.1.0 -11.568  -4.5677 -0.5677  4.432 12.432
## nodefactor.deg.main.deg.pers.1.1 -23.364  -8.3643 -0.3643  8.636 24.636
## nodefactor.deg.main.deg.pers.1.2 -24.870  -8.8703  0.1297  8.130 26.130
## nodefactor.race..wa.B            -11.186  -0.1865  5.8135 11.814 23.814
## nodefactor.race..wa.H            -15.835  -1.8353  5.1647 13.165 27.165
## nodematch.race..wa.B              -2.538  -2.5375 -2.5375 -2.538 -2.538
## nodematch.race..wa.H             -10.275  -7.2750 -6.2750 -4.275 -0.275
## nodematch.race..wa.O             -26.884  -5.8841  6.1159 17.116 40.116
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                                      edges
## edges                            1.0000000
## nodefactor.deg.main.deg.pers.0.1 0.5601332
## nodefactor.deg.main.deg.pers.0.2 0.2714288
## nodefactor.deg.main.deg.pers.1.0 0.2759422
## nodefactor.deg.main.deg.pers.1.1 0.5028595
## nodefactor.deg.main.deg.pers.1.2 0.5091837
## nodefactor.race..wa.B            0.4171243
## nodefactor.race..wa.H            0.4418825
## nodematch.race..wa.B                    NA
## nodematch.race..wa.H             0.1194725
## nodematch.race..wa.O             0.7870153
##                                  nodefactor.deg.main.deg.pers.0.1
## edges                                                  0.56013323
## nodefactor.deg.main.deg.pers.0.1                       1.00000000
## nodefactor.deg.main.deg.pers.0.2                       0.07137914
## nodefactor.deg.main.deg.pers.1.0                       0.08425267
## nodefactor.deg.main.deg.pers.1.1                       0.14271804
## nodefactor.deg.main.deg.pers.1.2                       0.13628091
## nodefactor.race..wa.B                                  0.25599109
## nodefactor.race..wa.H                                  0.23286265
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.06479252
## nodematch.race..wa.O                                   0.43815248
##                                  nodefactor.deg.main.deg.pers.0.2
## edges                                                  0.27142882
## nodefactor.deg.main.deg.pers.0.1                       0.07137914
## nodefactor.deg.main.deg.pers.0.2                       1.00000000
## nodefactor.deg.main.deg.pers.1.0                       0.03479577
## nodefactor.deg.main.deg.pers.1.1                       0.07896060
## nodefactor.deg.main.deg.pers.1.2                       0.06519433
## nodefactor.race..wa.B                                  0.12338815
## nodefactor.race..wa.H                                  0.11397616
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.03222544
## nodematch.race..wa.O                                   0.21206716
##                                  nodefactor.deg.main.deg.pers.1.0
## edges                                                  0.27594216
## nodefactor.deg.main.deg.pers.0.1                       0.08425267
## nodefactor.deg.main.deg.pers.0.2                       0.03479577
## nodefactor.deg.main.deg.pers.1.0                       1.00000000
## nodefactor.deg.main.deg.pers.1.1                       0.07251766
## nodefactor.deg.main.deg.pers.1.2                       0.06895669
## nodefactor.race..wa.B                                  0.10328107
## nodefactor.race..wa.H                                  0.12863974
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.03636133
## nodematch.race..wa.O                                   0.21963029
##                                  nodefactor.deg.main.deg.pers.1.1
## edges                                                  0.50285950
## nodefactor.deg.main.deg.pers.0.1                       0.14271804
## nodefactor.deg.main.deg.pers.0.2                       0.07896060
## nodefactor.deg.main.deg.pers.1.0                       0.07251766
## nodefactor.deg.main.deg.pers.1.1                       1.00000000
## nodefactor.deg.main.deg.pers.1.2                       0.13174709
## nodefactor.race..wa.B                                  0.17847020
## nodefactor.race..wa.H                                  0.21371241
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.05174888
## nodematch.race..wa.O                                   0.41645037
##                                  nodefactor.deg.main.deg.pers.1.2
## edges                                                  0.50918368
## nodefactor.deg.main.deg.pers.0.1                       0.13628091
## nodefactor.deg.main.deg.pers.0.2                       0.06519433
## nodefactor.deg.main.deg.pers.1.0                       0.06895669
## nodefactor.deg.main.deg.pers.1.1                       0.13174709
## nodefactor.deg.main.deg.pers.1.2                       1.00000000
## nodefactor.race..wa.B                                  0.18916310
## nodefactor.race..wa.H                                  0.26356744
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.07792721
## nodematch.race..wa.O                                   0.39082433
##                                  nodefactor.race..wa.B
## edges                                      0.417124271
## nodefactor.deg.main.deg.pers.0.1           0.255991085
## nodefactor.deg.main.deg.pers.0.2           0.123388148
## nodefactor.deg.main.deg.pers.1.0           0.103281068
## nodefactor.deg.main.deg.pers.1.1           0.178470198
## nodefactor.deg.main.deg.pers.1.2           0.189163103
## nodefactor.race..wa.B                      1.000000000
## nodefactor.race..wa.H                     -0.003288847
## nodematch.race..wa.B                                NA
## nodematch.race..wa.H                       0.002747197
## nodematch.race..wa.O                       0.005310808
##                                  nodefactor.race..wa.H
## edges                                      0.441882467
## nodefactor.deg.main.deg.pers.0.1           0.232862649
## nodefactor.deg.main.deg.pers.0.2           0.113976163
## nodefactor.deg.main.deg.pers.1.0           0.128639738
## nodefactor.deg.main.deg.pers.1.1           0.213712413
## nodefactor.deg.main.deg.pers.1.2           0.263567440
## nodefactor.race..wa.B                     -0.003288847
## nodefactor.race..wa.H                      1.000000000
## nodematch.race..wa.B                                NA
## nodematch.race..wa.H                       0.495733897
## nodematch.race..wa.O                      -0.002201865
##                                  nodematch.race..wa.B nodematch.race..wa.H
## edges                                              NA          0.119472524
## nodefactor.deg.main.deg.pers.0.1                   NA          0.064792520
## nodefactor.deg.main.deg.pers.0.2                   NA          0.032225440
## nodefactor.deg.main.deg.pers.1.0                   NA          0.036361333
## nodefactor.deg.main.deg.pers.1.1                   NA          0.051748878
## nodefactor.deg.main.deg.pers.1.2                   NA          0.077927209
## nodefactor.race..wa.B                              NA          0.002747197
## nodefactor.race..wa.H                              NA          0.495733897
## nodematch.race..wa.B                                1                   NA
## nodematch.race..wa.H                               NA          1.000000000
## nodematch.race..wa.O                               NA         -0.008669426
##                                  nodematch.race..wa.O
## edges                                     0.787015294
## nodefactor.deg.main.deg.pers.0.1          0.438152482
## nodefactor.deg.main.deg.pers.0.2          0.212067162
## nodefactor.deg.main.deg.pers.1.0          0.219630291
## nodefactor.deg.main.deg.pers.1.1          0.416450370
## nodefactor.deg.main.deg.pers.1.2          0.390824329
## nodefactor.race..wa.B                     0.005310808
## nodefactor.race..wa.H                    -0.002201865
## nodematch.race..wa.B                               NA
## nodematch.race..wa.H                     -0.008669426
## nodematch.race..wa.O                      1.000000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.003263543                     -0.031492481
## Lag 2e+05 -0.000960946                      0.006988825
## Lag 3e+05  0.002966553                      0.009003673
## Lag 4e+05 -0.006399652                     -0.015414791
## Lag 5e+05 -0.008084676                     -0.014306557
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0086773540
## Lag 2e+05                    -0.0002903341
## Lag 3e+05                    -0.0234441222
## Lag 4e+05                     0.0192747318
## Lag 5e+05                    -0.0270200735
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                           1.00000000
## Lag 1e+05                      -0.01718846
## Lag 2e+05                      -0.01608196
## Lag 3e+05                       0.04066358
## Lag 4e+05                       0.02077820
## Lag 5e+05                      -0.01188138
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.031198865
## Lag 2e+05                      0.012334951
## Lag 3e+05                      0.001374661
## Lag 4e+05                     -0.014159130
## Lag 5e+05                      0.036058866
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                     -0.008174722          -0.005805584
## Lag 2e+05                     -0.003893731          -0.036307056
## Lag 3e+05                     -0.009316215          -0.007567145
## Lag 4e+05                      0.010438611           0.004178925
## Lag 5e+05                     -0.011314959           0.025905974
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN          1.000000000
## Lag 1e+05          -0.003828615                  NaN         -0.012570417
## Lag 2e+05          -0.008867698                  NaN          0.025563282
## Lag 3e+05          -0.005731050                  NaN          0.013429452
## Lag 4e+05           0.002027726                  NaN         -0.004304579
## Lag 5e+05           0.023477873                  NaN          0.022949063
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.014659939
## Lag 2e+05          0.010149369
## Lag 3e+05         -0.009267030
## Lag 4e+05         -0.005294072
## Lag 5e+05          0.002193857
## Chain 2 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.035406129                      0.005749568
## Lag 2e+05 -0.021357683                     -0.007223589
## Lag 3e+05 -0.014073849                     -0.003216375
## Lag 4e+05  0.005116832                     -0.002250077
## Lag 5e+05 -0.011261133                     -0.017385621
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.008712529
## Lag 2e+05                     -0.017318064
## Lag 3e+05                     -0.000732260
## Lag 4e+05                     -0.012515749
## Lag 5e+05                     -0.007133813
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.007060592
## Lag 2e+05                     -0.016195632
## Lag 3e+05                      0.017268577
## Lag 4e+05                      0.032368429
## Lag 5e+05                      0.007394705
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.002834630
## Lag 2e+05                     -0.001692535
## Lag 3e+05                      0.009794797
## Lag 4e+05                      0.005026994
## Lag 5e+05                     -0.044407513
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                      0.004223682          -0.005515692
## Lag 2e+05                     -0.001781449          -0.018841418
## Lag 3e+05                      0.003559444           0.001295144
## Lag 4e+05                     -0.001975882          -0.006754649
## Lag 5e+05                      0.035465141          -0.021011004
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN           1.00000000
## Lag 1e+05           0.009749477                  NaN          -0.03042835
## Lag 2e+05           0.014043630                  NaN          -0.01613967
## Lag 3e+05          -0.012732948                  NaN          -0.00534948
## Lag 4e+05           0.001109932                  NaN           0.01480938
## Lag 5e+05          -0.025688376                  NaN           0.01670770
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.008825420
## Lag 2e+05         -0.025938454
## Lag 3e+05         -0.018607369
## Lag 4e+05          0.001104774
## Lag 5e+05          0.030904239
## Chain 3 
##                   edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.0000000000                      1.000000000
## Lag 1e+05 -0.0047470592                     -0.018440412
## Lag 2e+05  0.0137951265                     -0.013405216
## Lag 3e+05  0.0234161237                      0.020375913
## Lag 4e+05  0.0009626197                      0.010562116
## Lag 5e+05 -0.0261694584                      0.008936712
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0183477892
## Lag 2e+05                    -0.0169897817
## Lag 3e+05                    -0.0059682717
## Lag 4e+05                    -0.0002367892
## Lag 5e+05                     0.0047053843
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.000000e+00
## Lag 1e+05                    -4.146255e-03
## Lag 2e+05                     6.622337e-03
## Lag 3e+05                    -2.004038e-02
## Lag 4e+05                     1.127230e-02
## Lag 5e+05                    -6.245828e-05
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.010231833
## Lag 2e+05                      0.028505444
## Lag 3e+05                      0.033256563
## Lag 4e+05                      0.008604250
## Lag 5e+05                     -0.005237767
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000          1.0000000000
## Lag 1e+05                      0.022776394          0.0081392672
## Lag 2e+05                      0.008177325         -0.0001069287
## Lag 3e+05                      0.004371953         -0.0150352332
## Lag 4e+05                      0.023281646          0.0120224838
## Lag 5e+05                     -0.023434770         -0.0306500886
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN          1.000000000
## Lag 1e+05          -0.010104469                  NaN          0.003659839
## Lag 2e+05          -0.021140203                  NaN          0.003565070
## Lag 3e+05           0.020465451                  NaN         -0.001725216
## Lag 4e+05           0.018253931                  NaN          0.019376256
## Lag 5e+05           0.007592792                  NaN         -0.026398520
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.004878704
## Lag 2e+05          0.006366966
## Lag 3e+05          0.036764629
## Lag 4e+05          0.002636258
## Lag 5e+05         -0.018327430
## Chain 4 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05 -0.002531248                      0.006319107
## Lag 2e+05 -0.011796605                     -0.002066789
## Lag 3e+05  0.014840811                      0.002803799
## Lag 4e+05 -0.010317062                     -0.008207676
## Lag 5e+05  0.001520677                      0.007172441
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.018654250
## Lag 2e+05                     -0.001484131
## Lag 3e+05                     -0.009058657
## Lag 4e+05                      0.003517378
## Lag 5e+05                     -0.008616816
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.006219228
## Lag 2e+05                     -0.011678744
## Lag 3e+05                     -0.008052535
## Lag 4e+05                     -0.039018503
## Lag 5e+05                      0.023993069
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.003857160
## Lag 2e+05                     -0.009434225
## Lag 3e+05                     -0.030670121
## Lag 4e+05                      0.004850767
## Lag 5e+05                     -0.030521676
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000            1.00000000
## Lag 1e+05                     -0.003898732           -0.02718692
## Lag 2e+05                     -0.019877398           -0.01133573
## Lag 3e+05                      0.024823076            0.01992790
## Lag 4e+05                     -0.007008696            0.01394109
## Lag 5e+05                      0.020755595           -0.00669053
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN          1.000000000
## Lag 1e+05           0.001437371                  NaN          0.004244873
## Lag 2e+05           0.009043652                  NaN         -0.011313711
## Lag 3e+05           0.004892896                  NaN          0.012552805
## Lag 4e+05          -0.009552151                  NaN         -0.001030845
## Lag 5e+05          -0.009461078                  NaN          0.015668314
##           nodematch.race..wa.O
## Lag 0             1.0000000000
## Lag 1e+05         0.0063793300
## Lag 2e+05        -0.0001685022
## Lag 3e+05         0.0089177684
## Lag 4e+05        -0.0212850838
## Lag 5e+05        -0.0116800132
## Chain 5 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.016192713                      0.012401134
## Lag 2e+05 -0.012760646                     -0.004856308
## Lag 3e+05  0.001878554                     -0.011987955
## Lag 4e+05 -0.016701194                      0.028152006
## Lag 5e+05 -0.006181976                      0.025153672
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                     -0.004678573
## Lag 2e+05                      0.007100388
## Lag 3e+05                      0.024197972
## Lag 4e+05                     -0.001169242
## Lag 5e+05                     -0.014810261
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.002217407
## Lag 2e+05                      0.013080852
## Lag 3e+05                     -0.024632425
## Lag 4e+05                      0.012929141
## Lag 5e+05                      0.014592882
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.001952058
## Lag 2e+05                      0.028095377
## Lag 3e+05                      0.015104246
## Lag 4e+05                     -0.006862831
## Lag 5e+05                     -0.034001919
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                      0.003188849          -0.001503055
## Lag 2e+05                     -0.007436318          -0.005577744
## Lag 3e+05                      0.017910098           0.006289457
## Lag 4e+05                      0.018260151          -0.022181114
## Lag 5e+05                      0.005441830           0.006788565
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN          1.000000000
## Lag 1e+05           0.006685837                  NaN         -0.018180982
## Lag 2e+05          -0.002697884                  NaN         -0.009766456
## Lag 3e+05           0.003965997                  NaN          0.018889661
## Lag 4e+05           0.005260389                  NaN         -0.005538738
## Lag 5e+05          -0.011365350                  NaN         -0.015179972
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.022150511
## Lag 2e+05         -0.004910913
## Lag 3e+05          0.017863772
## Lag 4e+05          0.013487372
## Lag 5e+05         -0.022911707
## Chain 6 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                     1.0000000000
## Lag 1e+05 -0.008806552                     0.0001296644
## Lag 2e+05 -0.019364598                     0.0027380583
## Lag 3e+05 -0.001772476                    -0.0126630530
## Lag 4e+05  0.008720581                     0.0127149116
## Lag 5e+05 -0.002764916                     0.0058995020
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                     -0.030186936
## Lag 2e+05                      0.016829321
## Lag 3e+05                     -0.001571619
## Lag 4e+05                      0.005095037
## Lag 5e+05                     -0.016096941
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.009736809
## Lag 2e+05                     -0.005264595
## Lag 3e+05                      0.013386446
## Lag 4e+05                     -0.004239663
## Lag 5e+05                      0.007265640
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.018691525
## Lag 2e+05                      0.019440747
## Lag 3e+05                     -0.003357697
## Lag 4e+05                      0.011739150
## Lag 5e+05                     -0.008798493
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                     -0.012550016          -0.008745848
## Lag 2e+05                     -0.004722481          -0.008298144
## Lag 3e+05                     -0.011960508          -0.011083076
## Lag 4e+05                     -0.001004909           0.003207517
## Lag 5e+05                     -0.028039003           0.011111779
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN          1.000000000
## Lag 1e+05          -0.052827700                  NaN         -0.018444288
## Lag 2e+05          -0.011920154                  NaN         -0.005288119
## Lag 3e+05          -0.011513453                  NaN         -0.027819924
## Lag 4e+05           0.005540105                  NaN          0.034080297
## Lag 5e+05           0.004567508                  NaN         -0.020029481
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.016781040
## Lag 2e+05         -0.017097375
## Lag 3e+05          0.008884166
## Lag 4e+05          0.025308595
## Lag 5e+05          0.007439243
## Chain 7 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                     1.0000000000
## Lag 1e+05 -0.004026595                     0.0135524609
## Lag 2e+05  0.006444475                     0.0005624462
## Lag 3e+05  0.013511247                    -0.0026964830
## Lag 4e+05  0.001043332                    -0.0066436201
## Lag 5e+05 -0.016615236                    -0.0105483732
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.004218392
## Lag 2e+05                     -0.001823677
## Lag 3e+05                      0.012882662
## Lag 4e+05                     -0.023948954
## Lag 5e+05                     -0.021665809
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.010355376
## Lag 2e+05                      0.005911978
## Lag 3e+05                      0.002884730
## Lag 4e+05                      0.005893478
## Lag 5e+05                     -0.013912435
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0151243691
## Lag 2e+05                     0.0002728808
## Lag 3e+05                    -0.0200123451
## Lag 4e+05                    -0.0026173031
## Lag 5e+05                     0.0088408013
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000          1.0000000000
## Lag 1e+05                     -0.010726183          0.0155439543
## Lag 2e+05                      0.006653098          0.0115415540
## Lag 3e+05                      0.005990014         -0.0007629631
## Lag 4e+05                     -0.030022652          0.0194821530
## Lag 5e+05                     -0.017312348         -0.0046205172
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN           1.00000000
## Lag 1e+05          -0.004115470                  NaN          -0.01313849
## Lag 2e+05          -0.007466682                  NaN           0.01621809
## Lag 3e+05           0.021488811                  NaN           0.01362393
## Lag 4e+05          -0.030225442                  NaN          -0.00361950
## Lag 5e+05          -0.010734358                  NaN          -0.01635600
##           nodematch.race..wa.O
## Lag 0             1.0000000000
## Lag 1e+05         0.0083226739
## Lag 2e+05         0.0149701434
## Lag 3e+05         0.0027523274
## Lag 4e+05         0.0160455149
## Lag 5e+05         0.0007560433
## Chain 8 
##                   edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.0000000000                      1.000000000
## Lag 1e+05  0.0155696255                     -0.003158309
## Lag 2e+05  0.0073081962                      0.016221771
## Lag 3e+05  0.0324023504                     -0.028866226
## Lag 4e+05  0.0020793748                      0.009788450
## Lag 5e+05 -0.0006283994                      0.016752136
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0060613870
## Lag 2e+05                     0.0079733579
## Lag 3e+05                     0.0492074440
## Lag 4e+05                    -0.0001792147
## Lag 5e+05                     0.0045428013
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0124513122
## Lag 2e+05                    -0.0163824589
## Lag 3e+05                    -0.0211378711
## Lag 4e+05                     0.0307575558
## Lag 5e+05                     0.0006533357
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.010345641
## Lag 2e+05                      0.008247153
## Lag 3e+05                      0.046402065
## Lag 4e+05                     -0.005922073
## Lag 5e+05                     -0.008056756
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                      0.001327784           0.020445349
## Lag 2e+05                     -0.006652760           0.017909845
## Lag 3e+05                      0.014781427           0.013439996
## Lag 4e+05                      0.023250075          -0.018613581
## Lag 5e+05                     -0.043306963          -0.006293157
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN          1.000000000
## Lag 1e+05          -0.006237192                  NaN          0.011657077
## Lag 2e+05          -0.010931640                  NaN          0.019728118
## Lag 3e+05           0.037530542                  NaN          0.022795046
## Lag 4e+05          -0.007301601                  NaN         -0.000486767
## Lag 5e+05           0.014375116                  NaN         -0.024078580
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.020717617
## Lag 2e+05          0.015150776
## Lag 3e+05          0.011905116
## Lag 4e+05          0.008829357
## Lag 5e+05         -0.003141713
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.74047                          0.68037 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.35816                          0.07053 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.95443                         -1.06205 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          0.81024                          0.14860 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                         -0.66791 
##             nodematch.race..wa.O 
##                          0.29980 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.4590175                        0.4962724 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.7202200                        0.9437690 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.3398642                        0.2882123 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.4178006                        0.8818678 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                        0.5041890 
##             nodematch.race..wa.O 
##                        0.7643290 
## Joint P-value (lower = worse):  0.549409 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.19000                         -0.15430 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.13228                          0.70728 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          1.16700                          0.07413 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          0.31496                         -0.09462 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                          0.78103 
##             nodematch.race..wa.O 
##                          0.27909 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.8493086                        0.8773712 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.8947649                        0.4793925 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.2432095                        0.9409049 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.7527891                        0.9246147 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                        0.4347865 
##             nodematch.race..wa.O 
##                        0.7801751 
## Joint P-value (lower = worse):  0.958518 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           1.0325                           0.8050 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           0.3994                           0.2392 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          -0.6258                           0.5612 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                           1.0273                          -0.4226 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                          -0.6717 
##             nodematch.race..wa.O 
##                           1.0231 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.3018562                        0.4208115 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.6896189                        0.8109877 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.5314514                        0.5746642 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.3042678                        0.6725732 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                        0.5017750 
##             nodematch.race..wa.O 
##                        0.3062538 
## Joint P-value (lower = worse):  0.942607 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -0.03909                         -0.35928 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.46193                         -0.52027 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.03734                          0.25438 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          1.16287                         -0.56995 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                          0.14842 
##             nodematch.race..wa.O 
##                         -0.15752 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.9688170                        0.7193889 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.6441334                        0.6028771 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.9702111                        0.7992029 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.2448825                        0.5687129 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                        0.8820092 
##             nodematch.race..wa.O 
##                        0.8748362 
## Joint P-value (lower = worse):  0.9895132 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -0.76359                          0.01195 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          0.48513                          0.47522 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.88226                         -1.75650 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                         -0.68624                         -0.74270 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                         -0.23717 
##             nodematch.race..wa.O 
##                         -0.22617 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.44511163                       0.99046505 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.62758666                       0.63462895 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.37763631                       0.07900388 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.49256172                       0.45766349 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.81252570 
##             nodematch.race..wa.O 
##                       0.82106698 
## Joint P-value (lower = worse):  0.7513384 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          -0.3985                          -0.1986 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          -0.8862                          -0.6987 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           0.9570                           0.2141 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          -0.6654                          -1.3768 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                          -0.4450 
##             nodematch.race..wa.O 
##                           0.5314 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.6902696                        0.8426001 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.3754989                        0.4847622 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.3385450                        0.8304433 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.5057845                        0.1685770 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                        0.6562874 
##             nodematch.race..wa.O 
##                        0.5951139 
## Joint P-value (lower = worse):  0.8754277 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           0.7209                           0.8497 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           1.0599                           0.7305 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           2.0799                           0.6182 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                           1.5869                           0.8858 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                           0.0991 
##             nodematch.race..wa.O 
##                          -0.4953 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.47097943                       0.39550696 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.28917312                       0.46507611 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.03753794                       0.53644669 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.11253488                       0.37572961 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.92105781 
##             nodematch.race..wa.O 
##                       0.62041074 
## Joint P-value (lower = worse):  0.4454221 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.32079                         -0.62566 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          2.04430                          2.42108 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.36164                         -0.09019 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                         -0.20454                          0.81291 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                          2.05272 
##             nodematch.race..wa.O 
##                          0.32132 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.74836731                       0.53153730 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.04092354                       0.01547440 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.71762244                       0.92813820 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.83793289                       0.41627038 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.04009959 
##             nodematch.race..wa.O 
##                       0.74796644 
## Joint P-value (lower = worse):  0.2574 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 5

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                                      Mean     SD Naive SE Time-series SE
## edges                            -4.19363 21.797  0.12585        0.12694
## nodefactor.deg.main.deg.pers.0.1  0.54969 14.389  0.08307        0.08225
## nodefactor.deg.main.deg.pers.0.2  0.22136  6.216  0.03589        0.03589
## nodefactor.deg.main.deg.pers.1.0 -0.03198  6.191  0.03574        0.03551
## nodefactor.deg.main.deg.pers.1.1  0.72265 12.457  0.07192        0.07192
## nodefactor.deg.main.deg.pers.1.2  0.60976 12.974  0.07490        0.07414
## nodefactor.race..wa.B             6.29804  8.913  0.05146        0.05162
## nodefactor.race..wa.H             6.55839 11.060  0.06386        0.06399
## nodefactor.region.EW              0.20375  9.596  0.05540        0.05489
## nodefactor.region.OW              0.74111 17.505  0.10107        0.10059
## nodematch.race..wa.B             -2.53754  0.000  0.00000        0.00000
## nodematch.race..wa.H             -5.57060  2.774  0.01601        0.01619
## nodematch.race..wa.O              6.26062 17.217  0.09940        0.09856
## 
## 2. Quantiles for each variable:
## 
##                                     2.5%      25%     50%    75%  97.5%
## edges                            -46.512 -18.5122 -4.5122 10.488 38.488
## nodefactor.deg.main.deg.pers.0.1 -27.175  -9.1754  0.8246  9.825 28.825
## nodefactor.deg.main.deg.pers.0.2 -11.217  -4.2169 -0.2169  4.783 12.783
## nodefactor.deg.main.deg.pers.1.0 -11.568  -4.5677 -0.5677  4.432 12.432
## nodefactor.deg.main.deg.pers.1.1 -23.364  -7.3643  0.6357  8.636 25.636
## nodefactor.deg.main.deg.pers.1.2 -24.870  -7.8703  0.1297  9.130 26.130
## nodefactor.race..wa.B            -10.186  -0.1865  5.8135 11.814 23.814
## nodefactor.race..wa.H            -14.835  -0.8353  6.1647 14.165 29.165
## nodefactor.region.EW             -18.389  -6.3887 -0.3887  6.611 19.611
## nodefactor.region.OW             -33.159 -11.1591  0.8409 12.841 35.841
## nodematch.race..wa.B              -2.538  -2.5375 -2.5375 -2.538 -2.538
## nodematch.race..wa.H             -10.275  -7.2750 -5.2750 -4.275  0.725
## nodematch.race..wa.O             -26.884  -5.8841  6.1159 18.116 40.116
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                                      edges
## edges                            1.0000000
## nodefactor.deg.main.deg.pers.0.1 0.5652485
## nodefactor.deg.main.deg.pers.0.2 0.2822276
## nodefactor.deg.main.deg.pers.1.0 0.2732382
## nodefactor.deg.main.deg.pers.1.1 0.4991270
## nodefactor.deg.main.deg.pers.1.2 0.5104651
## nodefactor.race..wa.B            0.4093834
## nodefactor.race..wa.H            0.4421720
## nodefactor.region.EW             0.4039847
## nodefactor.region.OW             0.6369959
## nodematch.race..wa.B                    NA
## nodematch.race..wa.H             0.1293270
## nodematch.race..wa.O             0.7908798
##                                  nodefactor.deg.main.deg.pers.0.1
## edges                                                  0.56524848
## nodefactor.deg.main.deg.pers.0.1                       1.00000000
## nodefactor.deg.main.deg.pers.0.2                       0.08007919
## nodefactor.deg.main.deg.pers.1.0                       0.07858225
## nodefactor.deg.main.deg.pers.1.1                       0.14014273
## nodefactor.deg.main.deg.pers.1.2                       0.14067499
## nodefactor.race..wa.B                                  0.24494320
## nodefactor.race..wa.H                                  0.24081257
## nodefactor.region.EW                                   0.23909230
## nodefactor.region.OW                                   0.37188296
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.07312426
## nodematch.race..wa.O                                   0.44589948
##                                  nodefactor.deg.main.deg.pers.0.2
## edges                                                  0.28222760
## nodefactor.deg.main.deg.pers.0.1                       0.08007919
## nodefactor.deg.main.deg.pers.0.2                       1.00000000
## nodefactor.deg.main.deg.pers.1.0                       0.03041187
## nodefactor.deg.main.deg.pers.1.1                       0.07710859
## nodefactor.deg.main.deg.pers.1.2                       0.07006828
## nodefactor.race..wa.B                                  0.12769306
## nodefactor.race..wa.H                                  0.11044998
## nodefactor.region.EW                                   0.11255732
## nodefactor.region.OW                                   0.17916971
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.03398996
## nodematch.race..wa.O                                   0.22572614
##                                  nodefactor.deg.main.deg.pers.1.0
## edges                                                  0.27323816
## nodefactor.deg.main.deg.pers.0.1                       0.07858225
## nodefactor.deg.main.deg.pers.0.2                       0.03041187
## nodefactor.deg.main.deg.pers.1.0                       1.00000000
## nodefactor.deg.main.deg.pers.1.1                       0.07852927
## nodefactor.deg.main.deg.pers.1.2                       0.06897309
## nodefactor.race..wa.B                                  0.10114552
## nodefactor.race..wa.H                                  0.12789937
## nodefactor.region.EW                                   0.10364036
## nodefactor.region.OW                                   0.16192582
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.03940884
## nodematch.race..wa.O                                   0.21775180
##                                  nodefactor.deg.main.deg.pers.1.1
## edges                                                  0.49912702
## nodefactor.deg.main.deg.pers.0.1                       0.14014273
## nodefactor.deg.main.deg.pers.0.2                       0.07710859
## nodefactor.deg.main.deg.pers.1.0                       0.07852927
## nodefactor.deg.main.deg.pers.1.1                       1.00000000
## nodefactor.deg.main.deg.pers.1.2                       0.11909869
## nodefactor.race..wa.B                                  0.18374560
## nodefactor.race..wa.H                                  0.20952060
## nodefactor.region.EW                                   0.18254195
## nodefactor.region.OW                                   0.27577812
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.05473472
## nodematch.race..wa.O                                   0.41100812
##                                  nodefactor.deg.main.deg.pers.1.2
## edges                                                  0.51046508
## nodefactor.deg.main.deg.pers.0.1                       0.14067499
## nodefactor.deg.main.deg.pers.0.2                       0.07006828
## nodefactor.deg.main.deg.pers.1.0                       0.06897309
## nodefactor.deg.main.deg.pers.1.1                       0.11909869
## nodefactor.deg.main.deg.pers.1.2                       1.00000000
## nodefactor.race..wa.B                                  0.18986350
## nodefactor.race..wa.H                                  0.26599005
## nodefactor.region.EW                                   0.22166579
## nodefactor.region.OW                                   0.31766397
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.09371512
## nodematch.race..wa.O                                   0.39219894
##                                  nodefactor.race..wa.B
## edges                                     0.4093833672
## nodefactor.deg.main.deg.pers.0.1          0.2449432006
## nodefactor.deg.main.deg.pers.0.2          0.1276930582
## nodefactor.deg.main.deg.pers.1.0          0.1011455214
## nodefactor.deg.main.deg.pers.1.1          0.1837456016
## nodefactor.deg.main.deg.pers.1.2          0.1898634956
## nodefactor.race..wa.B                     1.0000000000
## nodefactor.race..wa.H                    -0.0033484239
## nodefactor.region.EW                      0.0979253367
## nodefactor.region.OW                      0.2385142156
## nodematch.race..wa.B                                NA
## nodematch.race..wa.H                      0.0005923706
## nodematch.race..wa.O                      0.0028577450
##                                  nodefactor.race..wa.H
## edges                                     4.421720e-01
## nodefactor.deg.main.deg.pers.0.1          2.408126e-01
## nodefactor.deg.main.deg.pers.0.2          1.104500e-01
## nodefactor.deg.main.deg.pers.1.0          1.278994e-01
## nodefactor.deg.main.deg.pers.1.1          2.095206e-01
## nodefactor.deg.main.deg.pers.1.2          2.659901e-01
## nodefactor.race..wa.B                    -3.348424e-03
## nodefactor.race..wa.H                     1.000000e+00
## nodefactor.region.EW                      2.965637e-01
## nodefactor.region.OW                      2.674665e-01
## nodematch.race..wa.B                                NA
## nodematch.race..wa.H                      5.025899e-01
## nodematch.race..wa.O                      9.944499e-05
##                                  nodefactor.region.EW nodefactor.region.OW
## edges                                      0.40398474           0.63699592
## nodefactor.deg.main.deg.pers.0.1           0.23909230           0.37188296
## nodefactor.deg.main.deg.pers.0.2           0.11255732           0.17916971
## nodefactor.deg.main.deg.pers.1.0           0.10364036           0.16192582
## nodefactor.deg.main.deg.pers.1.1           0.18254195           0.27577812
## nodefactor.deg.main.deg.pers.1.2           0.22166579           0.31766397
## nodefactor.race..wa.B                      0.09792534           0.23851422
## nodefactor.race..wa.H                      0.29656375           0.26746652
## nodefactor.region.EW                       1.00000000           0.12443976
## nodefactor.region.OW                       0.12443976           1.00000000
## nodematch.race..wa.B                               NA                   NA
## nodematch.race..wa.H                       0.11292622           0.07673365
## nodematch.race..wa.O                       0.28844067           0.52352072
##                                  nodematch.race..wa.B nodematch.race..wa.H
## edges                                              NA         0.1293270415
## nodefactor.deg.main.deg.pers.0.1                   NA         0.0731242571
## nodefactor.deg.main.deg.pers.0.2                   NA         0.0339899632
## nodefactor.deg.main.deg.pers.1.0                   NA         0.0394088380
## nodefactor.deg.main.deg.pers.1.1                   NA         0.0547347244
## nodefactor.deg.main.deg.pers.1.2                   NA         0.0937151211
## nodefactor.race..wa.B                              NA         0.0005923706
## nodefactor.race..wa.H                              NA         0.5025899479
## nodefactor.region.EW                               NA         0.1129262211
## nodefactor.region.OW                               NA         0.0767336478
## nodematch.race..wa.B                                1                   NA
## nodematch.race..wa.H                               NA         1.0000000000
## nodematch.race..wa.O                               NA         0.0016622237
##                                  nodematch.race..wa.O
## edges                                    7.908798e-01
## nodefactor.deg.main.deg.pers.0.1         4.458995e-01
## nodefactor.deg.main.deg.pers.0.2         2.257261e-01
## nodefactor.deg.main.deg.pers.1.0         2.177518e-01
## nodefactor.deg.main.deg.pers.1.1         4.110081e-01
## nodefactor.deg.main.deg.pers.1.2         3.921989e-01
## nodefactor.race..wa.B                    2.857745e-03
## nodefactor.race..wa.H                    9.944499e-05
## nodefactor.region.EW                     2.884407e-01
## nodefactor.region.OW                     5.235207e-01
## nodematch.race..wa.B                               NA
## nodematch.race..wa.H                     1.662224e-03
## nodematch.race..wa.O                     1.000000e+00
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.004408803                      0.021847213
## Lag 2e+05  0.011046968                      0.009593305
## Lag 3e+05  0.008435084                     -0.012220016
## Lag 4e+05 -0.032557670                     -0.003099623
## Lag 5e+05 -0.002756434                      0.016223078
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.009132877
## Lag 2e+05                     -0.002049675
## Lag 3e+05                     -0.014425970
## Lag 4e+05                     -0.018530502
## Lag 5e+05                      0.008662617
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.004627539
## Lag 2e+05                     -0.016583603
## Lag 3e+05                     -0.019071780
## Lag 4e+05                     -0.009837854
## Lag 5e+05                     -0.004153599
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.008800654
## Lag 2e+05                      0.015673851
## Lag 3e+05                      0.023297830
## Lag 4e+05                      0.006933801
## Lag 5e+05                      0.002073913
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                     -0.009483402           0.006183689
## Lag 2e+05                      0.004909858           0.003312812
## Lag 3e+05                     -0.007265956          -0.013443996
## Lag 4e+05                     -0.014384301          -0.012995048
## Lag 5e+05                      0.012858449          -0.008858417
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000          1.000000000
## Lag 1e+05         -0.0006230853         -0.020406211          0.006352770
## Lag 2e+05         -0.0184831919          0.018377004         -0.021652561
## Lag 3e+05         -0.0218443394         -0.002652334          0.007622164
## Lag 4e+05         -0.0028031735          0.005413128         -0.012284751
## Lag 5e+05         -0.0037315048         -0.011030192         -0.019982387
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.001189914         -0.003794545
## Lag 2e+05                  NaN         -0.012999648          0.013005050
## Lag 3e+05                  NaN         -0.007251447          0.022305232
## Lag 4e+05                  NaN          0.025399585         -0.009483293
## Lag 5e+05                  NaN          0.018932433         -0.006150889
## Chain 2 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.013144899                      0.004181882
## Lag 2e+05 -0.022440569                     -0.031057328
## Lag 3e+05  0.004206147                     -0.027259651
## Lag 4e+05 -0.007478419                     -0.010749075
## Lag 5e+05 -0.008059571                     -0.016983572
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.001409496
## Lag 2e+05                      0.021181523
## Lag 3e+05                     -0.009296457
## Lag 4e+05                     -0.013567982
## Lag 5e+05                     -0.035850198
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.027582621
## Lag 2e+05                      0.017004925
## Lag 3e+05                      0.017326475
## Lag 4e+05                     -0.007874591
## Lag 5e+05                      0.010241195
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.003854792
## Lag 2e+05                     -0.002638751
## Lag 3e+05                     -0.008402796
## Lag 4e+05                      0.002498310
## Lag 5e+05                      0.013213020
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                     -0.024077915           0.011004571
## Lag 2e+05                     -0.013272267           0.013927954
## Lag 3e+05                      0.011180615           0.015088593
## Lag 4e+05                     -0.002072093          -0.008595558
## Lag 5e+05                      0.003320106           0.009370411
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000          1.000000000          1.000000000
## Lag 1e+05           -0.01854612         -0.002904527         -0.034801828
## Lag 2e+05            0.01543778         -0.010767616         -0.010705323
## Lag 3e+05            0.01204741         -0.011175965          0.006876874
## Lag 4e+05            0.01566985          0.019536280          0.016846473
## Lag 5e+05            0.01686364          0.006145531         -0.024580472
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000           1.00000000
## Lag 1e+05                  NaN          0.033077212           0.01216770
## Lag 2e+05                  NaN         -0.001662741          -0.01929117
## Lag 3e+05                  NaN         -0.020688167          -0.01076396
## Lag 4e+05                  NaN          0.021044733          -0.02634214
## Lag 5e+05                  NaN          0.020721525          -0.02292867
## Chain 3 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.013936926                      0.022966090
## Lag 2e+05 -0.002766157                     -0.016000510
## Lag 3e+05 -0.007366110                     -0.005355045
## Lag 4e+05  0.003258313                     -0.008277261
## Lag 5e+05  0.012099846                     -0.002604064
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0004492531
## Lag 2e+05                     0.0038336181
## Lag 3e+05                     0.0267935986
## Lag 4e+05                     0.0205876458
## Lag 5e+05                    -0.0110025298
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.009891949
## Lag 2e+05                      0.005964676
## Lag 3e+05                      0.005430987
## Lag 4e+05                      0.004155003
## Lag 5e+05                      0.014855460
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.007216891
## Lag 2e+05                     -0.008699936
## Lag 3e+05                      0.007168501
## Lag 4e+05                      0.015354878
## Lag 5e+05                      0.002635049
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                      0.009205539           0.002876198
## Lag 2e+05                      0.003265131           0.008308411
## Lag 3e+05                      0.013656536          -0.020596449
## Lag 4e+05                     -0.024948854           0.003057415
## Lag 5e+05                     -0.015905562          -0.006800265
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000         1.0000000000
## Lag 1e+05           0.027744710        -0.0156982622         0.0229332622
## Lag 2e+05           0.022315422         0.0005044831         0.0003987712
## Lag 3e+05          -0.034002649        -0.0149960716         0.0088896371
## Lag 4e+05           0.008439452         0.0006722239         0.0135233094
## Lag 5e+05           0.024053997        -0.0081178627         0.0279940606
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.012678015          0.032344707
## Lag 2e+05                  NaN          0.013293651         -0.004247220
## Lag 3e+05                  NaN         -0.003148378          0.008890546
## Lag 4e+05                  NaN         -0.011061385         -0.009285504
## Lag 5e+05                  NaN         -0.007372540         -0.005422188
## Chain 4 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                     1.0000000000
## Lag 1e+05 -0.017569789                     0.0135810639
## Lag 2e+05 -0.002089649                     0.0002329469
## Lag 3e+05  0.027926403                     0.0156160304
## Lag 4e+05  0.004628454                    -0.0010185090
## Lag 5e+05  0.015290693                     0.0074772258
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.005140564
## Lag 2e+05                     -0.004002997
## Lag 3e+05                     -0.003463366
## Lag 4e+05                      0.009630499
## Lag 5e+05                     -0.018558418
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0153498465
## Lag 2e+05                     0.0006118448
## Lag 3e+05                     0.0107360979
## Lag 4e+05                    -0.0346214797
## Lag 5e+05                    -0.0008597004
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.013633392
## Lag 2e+05                      0.008827248
## Lag 3e+05                      0.031279550
## Lag 4e+05                      0.010085732
## Lag 5e+05                     -0.010256584
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                     -0.026936205          -0.035963699
## Lag 2e+05                     -0.010674413          -0.004637310
## Lag 3e+05                      0.012082565           0.004951477
## Lag 4e+05                      0.001603205          -0.007354444
## Lag 5e+05                     -0.005273472           0.002668715
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.015578317         -0.024449665          0.012238604
## Lag 2e+05           0.014247679         -0.009822856          0.001376408
## Lag 3e+05           0.008027207          0.012862921         -0.001744527
## Lag 4e+05           0.013707834         -0.004317860         -0.009054175
## Lag 5e+05           0.014378099         -0.010928463          0.024052224
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.022429539         -0.017130136
## Lag 2e+05                  NaN          0.006529393         -0.017601085
## Lag 3e+05                  NaN          0.000148419          0.014485984
## Lag 4e+05                  NaN         -0.018147344         -0.003376153
## Lag 5e+05                  NaN         -0.005658106          0.013440723
## Chain 5 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                       1.00000000
## Lag 1e+05  0.023291773                       0.02129490
## Lag 2e+05  0.005619267                       0.02255089
## Lag 3e+05  0.009454548                       0.01003475
## Lag 4e+05 -0.008629484                       0.01125333
## Lag 5e+05  0.018959134                       0.00532500
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0064496859
## Lag 2e+05                    -0.0002627091
## Lag 3e+05                    -0.0207196078
## Lag 4e+05                    -0.0141465804
## Lag 5e+05                     0.0125271296
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.007728082
## Lag 2e+05                      0.013935317
## Lag 3e+05                      0.002155501
## Lag 4e+05                      0.002840733
## Lag 5e+05                      0.009067254
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.008132107
## Lag 2e+05                     -0.027586770
## Lag 3e+05                      0.026154416
## Lag 4e+05                      0.001840309
## Lag 5e+05                     -0.005258293
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                      0.008972445           0.029883283
## Lag 2e+05                      0.003220724           0.023141973
## Lag 3e+05                     -0.001512831           0.004760106
## Lag 4e+05                     -0.020389777           0.022140817
## Lag 5e+05                      0.002913525           0.035485689
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000          1.000000000
## Lag 1e+05          0.0154588863          0.007752573         -0.005303602
## Lag 2e+05          0.0011179697          0.018975278          0.011257456
## Lag 3e+05          0.0099664453         -0.003761075          0.037622204
## Lag 4e+05         -0.0072500278         -0.018102431         -0.010646220
## Lag 5e+05         -0.0002733342          0.006710798          0.016824524
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.013029322          0.020519988
## Lag 2e+05                  NaN          0.038707870          0.001196092
## Lag 3e+05                  NaN         -0.019445718          0.001194106
## Lag 4e+05                  NaN         -0.024348948         -0.012724743
## Lag 5e+05                  NaN         -0.009790533          0.007654802
## Chain 6 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                     1.0000000000
## Lag 1e+05 -0.016635594                     0.0035368986
## Lag 2e+05  0.035520925                     0.0174808992
## Lag 3e+05 -0.004706651                     0.0005283090
## Lag 4e+05  0.005123158                     0.0009582252
## Lag 5e+05  0.003282313                    -0.0205966667
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0051083065
## Lag 2e+05                    -0.0056848867
## Lag 3e+05                    -0.0004500454
## Lag 4e+05                     0.0045843106
## Lag 5e+05                    -0.0093713703
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.009804727
## Lag 2e+05                     -0.009405320
## Lag 3e+05                     -0.047188844
## Lag 4e+05                     -0.022465323
## Lag 5e+05                     -0.015915437
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0187065725
## Lag 2e+05                     0.0149505617
## Lag 3e+05                     0.0005554036
## Lag 4e+05                    -0.0125951969
## Lag 5e+05                     0.0187216374
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000          1.0000000000
## Lag 1e+05                      0.031875033         -0.0041998029
## Lag 2e+05                     -0.012365191          0.0005497124
## Lag 3e+05                     -0.006567242         -0.0106279851
## Lag 4e+05                      0.003806141         -0.0159248889
## Lag 5e+05                     -0.009073454          0.0031138183
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000         1.0000000000
## Lag 1e+05           0.002542687         0.0141455794        -0.0214357905
## Lag 2e+05           0.008720920         0.0007538431        -0.0008994087
## Lag 3e+05          -0.021242283         0.0124842672         0.0036151554
## Lag 4e+05          -0.001461241         0.0075220754         0.0307924899
## Lag 5e+05           0.006751540         0.0111326449        -0.0129430017
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.004179914         -0.010066814
## Lag 2e+05                  NaN          0.015485236          0.038138906
## Lag 3e+05                  NaN          0.016421090         -0.005318257
## Lag 4e+05                  NaN          0.008770505          0.003346465
## Lag 5e+05                  NaN         -0.036918514          0.013229124
## Chain 7 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                     1.0000000000
## Lag 1e+05  0.025876561                     0.0249993463
## Lag 2e+05 -0.021736530                    -0.0151130029
## Lag 3e+05  0.008337053                     0.0009708044
## Lag 4e+05  0.029666430                     0.0168255059
## Lag 5e+05  0.017682570                    -0.0162595623
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                           1.00000000
## Lag 1e+05                      -0.01861627
## Lag 2e+05                      -0.01041266
## Lag 3e+05                      -0.02206965
## Lag 4e+05                      -0.01547276
## Lag 5e+05                       0.01236251
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.008837403
## Lag 2e+05                      0.017624716
## Lag 3e+05                     -0.016431411
## Lag 4e+05                     -0.020810861
## Lag 5e+05                      0.020673398
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.008617843
## Lag 2e+05                      0.002933812
## Lag 3e+05                      0.012042560
## Lag 4e+05                      0.021582231
## Lag 5e+05                     -0.009858680
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                     -0.007857164           0.030279458
## Lag 2e+05                     -0.015781244           0.002009358
## Lag 3e+05                     -0.012777915           0.012606891
## Lag 4e+05                      0.023141370           0.008335398
## Lag 5e+05                     -0.006583357          -0.005913246
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000         1.0000000000
## Lag 1e+05          0.0007588585         -0.002575717        -0.0001369712
## Lag 2e+05         -0.0204936581         -0.022554016        -0.0167178602
## Lag 3e+05          0.0074964145         -0.001927445         0.0011314721
## Lag 4e+05          0.0095639671          0.001487305         0.0134915700
## Lag 5e+05         -0.0263685702          0.005432186         0.0083925457
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.005046048          0.020104910
## Lag 2e+05                  NaN          0.007571232         -0.031510381
## Lag 3e+05                  NaN          0.014302437         -0.008963648
## Lag 4e+05                  NaN          0.003617738          0.015684908
## Lag 5e+05                  NaN          0.001082283          0.020813809
## Chain 8 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.014639116                      0.028210719
## Lag 2e+05 -0.008063000                     -0.001019846
## Lag 3e+05 -0.002075622                      0.011135835
## Lag 4e+05 -0.017873309                      0.007768939
## Lag 5e+05 -0.008242085                     -0.013852857
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                     -0.012296959
## Lag 2e+05                     -0.001805836
## Lag 3e+05                      0.007299742
## Lag 4e+05                     -0.012963159
## Lag 5e+05                     -0.016905106
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.003650985
## Lag 2e+05                      0.009991197
## Lag 3e+05                     -0.022123460
## Lag 4e+05                     -0.002884759
## Lag 5e+05                      0.008076035
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.008761188
## Lag 2e+05                     -0.022203550
## Lag 3e+05                      0.006645297
## Lag 4e+05                     -0.002267879
## Lag 5e+05                     -0.018318936
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000            1.00000000
## Lag 1e+05                      0.002879269           -0.01697975
## Lag 2e+05                     -0.035994758           -0.01539481
## Lag 3e+05                     -0.032905029           -0.01421289
## Lag 4e+05                     -0.001715361            0.01085179
## Lag 5e+05                     -0.001957092            0.02587172
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000           1.00000000
## Lag 1e+05           0.030214854           0.01402675           0.00589752
## Lag 2e+05          -0.003769400          -0.03647840          -0.02113074
## Lag 3e+05          -0.007614316          -0.03030370           0.03521800
## Lag 4e+05          -0.002395037           0.01508648          -0.02435567
## Lag 5e+05          -0.040582727          -0.00468781          -0.01656606
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN         1.0000000000          1.000000000
## Lag 1e+05                  NaN        -0.0112326093          0.001366477
## Lag 2e+05                  NaN        -0.0009367922         -0.014942297
## Lag 3e+05                  NaN         0.0086593838          0.006532705
## Lag 4e+05                  NaN         0.0013814051         -0.013476941
## Lag 5e+05                  NaN        -0.0057276928         -0.023947070
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.60859                         -1.10954 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          0.26383                         -0.60850 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          1.81034                          1.89054 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          1.94175                          0.64537 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          0.32950                         -0.09244 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                         -0.64982 
##             nodematch.race..wa.O 
##                         -0.77013 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.54279692                       0.26719777 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.79191336                       0.54285658 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.07024357                       0.05868552 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.05216715                       0.51868836 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.74177859                       0.92634498 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.51580797 
##             nodematch.race..wa.O 
##                       0.44122220 
## Joint P-value (lower = worse):  0.2205978 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.67081                         -0.64046 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.71421                          2.21612 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          1.47737                         -0.61173 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          0.57395                          0.70488 
##             nodefactor.region.EW             nodefactor.region.OW 
##                         -0.05633                          0.73853 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                          0.55437 
##             nodematch.race..wa.O 
##                          0.21290 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.50234084                       0.52187569 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.47509448                       0.02668299 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.13957734                       0.54071472 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.56600070                       0.48088719 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.95507908                       0.46019033 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.57932285 
##             nodematch.race..wa.O 
##                       0.83140180 
## Joint P-value (lower = worse):  0.6484416 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          -1.9191                          -0.9312 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           1.0377                          -0.9880 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          -0.5420                          -0.6554 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          -1.3438                          -0.9422 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          -0.7808                          -0.3044 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                           0.8507 
##             nodematch.race..wa.O 
##                          -0.7713 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.05496751                       0.35172459 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.29940178                       0.32316571 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.58781494                       0.51218582 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.17900061                       0.34608384 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.43492037                       0.76085973 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.39491687 
##             nodematch.race..wa.O 
##                       0.44054766 
## Joint P-value (lower = worse):  0.430132 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         1.372015                         1.089303 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        -0.977236                        -0.225081 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         1.436118                        -0.400388 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                         0.369137                         1.825629 
##             nodefactor.region.EW             nodefactor.region.OW 
##                         0.776413                         2.559636 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                         0.006441 
##             nodematch.race..wa.O 
##                         0.333266 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.17005867                       0.27602043 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.32845220                       0.82191601 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.15096889                       0.68887085 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.71202562                       0.06790620 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.43750534                       0.01047819 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.99486091 
##             nodematch.race..wa.O 
##                       0.73893386 
## Joint P-value (lower = worse):  0.2544948 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           0.8249                           1.4951 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           0.5051                          -2.2141 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           0.4892                          -0.4064 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                           2.0592                          -1.1586 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          -1.0370                          -0.1444 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                          -1.4224 
##             nodematch.race..wa.O 
##                           0.4269 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.40945307                       0.13487624 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.61347963                       0.02681890 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.62471341                       0.68445847 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.03947741                       0.24663588 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.29973788                       0.88519853 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.15489975 
##             nodematch.race..wa.O 
##                       0.66948021 
## Joint P-value (lower = worse):  0.1243811 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           0.4859                           1.0706 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          -2.3455                           0.1330 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           0.9766                          -0.6670 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          -0.1664                           0.7706 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          -0.2788                          -1.0773 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                           1.5546 
##             nodematch.race..wa.O 
##                           0.4372 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.62702420                       0.28435051 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.01900354                       0.89423091 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.32875612                       0.50475061 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.86785301                       0.44095438 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.78038910                       0.28132958 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.12004513 
##             nodematch.race..wa.O 
##                       0.66199957 
## Joint P-value (lower = worse):  0.2285471 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           0.0682                           0.3026 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           1.6594                           0.3259 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           0.7779                          -0.5568 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          -0.9392                          -0.4104 
##             nodefactor.region.EW             nodefactor.region.OW 
##                           0.5429                           1.1518 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                           0.2005 
##             nodematch.race..wa.O 
##                           0.8721 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.94562716                       0.76221234 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.09703139                       0.74447281 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.43664898                       0.57767569 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.34761559                       0.68154302 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.58720646                       0.24939804 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.84111171 
##             nodematch.race..wa.O 
##                       0.38312780 
## Joint P-value (lower = worse):  0.6252442 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           1.0679                           1.1085 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          -0.2375                          -0.8394 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          -0.6268                           0.7491 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                           0.8530                           0.1264 
##             nodefactor.region.EW             nodefactor.region.OW 
##                           1.6602                           0.6473 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                           1.5318 
##             nodematch.race..wa.O 
##                           1.0357 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.28558292                       0.26765800 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.81223080                       0.40125011 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.53081414                       0.45378229 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.39367010                       0.89944978 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.09686806                       0.51741036 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.12557800 
##             nodematch.race..wa.O 
##                       0.30034038 
## Joint P-value (lower = worse):  0.6236519 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 6

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                                      Mean     SD Naive SE Time-series SE
## edges                            -5.40403 21.703  0.12530        0.12319
## nodefactor.deg.main.deg.pers.0.1  0.06846 14.218  0.08209        0.08175
## nodefactor.deg.main.deg.pers.0.2 -0.03144  6.247  0.03607        0.03601
## nodefactor.deg.main.deg.pers.1.0 -0.07208  6.128  0.03538        0.03510
## nodefactor.deg.main.deg.pers.1.1  0.17571 12.357  0.07134        0.07164
## nodefactor.deg.main.deg.pers.1.2  0.12369 12.927  0.07463        0.07420
## nodefactor.race..wa.B             6.03937  8.983  0.05186        0.05165
## nodefactor.race..wa.H             5.64675 10.947  0.06321        0.06310
## nodefactor.region.EW              0.07621  9.644  0.05568        0.05540
## nodefactor.region.OW              0.07544 17.389  0.10039        0.09985
## nodematch.race..wa.B             -2.53754  0.000  0.00000        0.00000
## nodematch.race..wa.H             -5.73306  2.742  0.01583        0.01586
## nodematch.race..wa.O              6.05805 17.081  0.09862        0.09841
## absdiff.sqrt.age                  0.31252 22.793  0.13160        0.13473
## 
## 2. Quantiles for each variable:
## 
##                                     2.5%      25%      50%    75%  97.5%
## edges                            -47.512 -20.5122 -5.51216  9.488 37.488
## nodefactor.deg.main.deg.pers.0.1 -27.175 -10.1754 -0.17541  9.825 28.825
## nodefactor.deg.main.deg.pers.0.2 -11.217  -4.2169 -0.21687  3.783 12.783
## nodefactor.deg.main.deg.pers.1.0 -11.568  -4.5677 -0.56768  4.432 12.432
## nodefactor.deg.main.deg.pers.1.1 -23.364  -8.3643 -0.36432  8.636 24.636
## nodefactor.deg.main.deg.pers.1.2 -24.870  -8.8703  0.12966  9.130 26.130
## nodefactor.race..wa.B            -11.186  -0.1865  5.81350 11.814 23.814
## nodefactor.race..wa.H            -14.835  -1.8353  5.16472 13.165 28.165
## nodefactor.region.EW             -18.389  -6.3887 -0.38872  6.611 19.611
## nodefactor.region.OW             -34.159 -12.1591 -0.15906 11.841 34.841
## nodematch.race..wa.B              -2.538  -2.5375 -2.53754 -2.538 -2.538
## nodematch.race..wa.H             -10.275  -7.2750 -6.27496 -4.275 -0.275
## nodematch.race..wa.O             -26.884  -5.8841  6.11592 17.116 40.116
## absdiff.sqrt.age                 -43.536 -15.3242  0.09694 15.562 45.333
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                                      edges
## edges                            1.0000000
## nodefactor.deg.main.deg.pers.0.1 0.5534125
## nodefactor.deg.main.deg.pers.0.2 0.2791760
## nodefactor.deg.main.deg.pers.1.0 0.2720727
## nodefactor.deg.main.deg.pers.1.1 0.4988419
## nodefactor.deg.main.deg.pers.1.2 0.5146578
## nodefactor.race..wa.B            0.4093242
## nodefactor.race..wa.H            0.4464807
## nodefactor.region.EW             0.4028496
## nodefactor.region.OW             0.6375991
## nodematch.race..wa.B                    NA
## nodematch.race..wa.H             0.1238437
## nodematch.race..wa.O             0.7890364
## absdiff.sqrt.age                 0.7671644
##                                  nodefactor.deg.main.deg.pers.0.1
## edges                                                  0.55341252
## nodefactor.deg.main.deg.pers.0.1                       1.00000000
## nodefactor.deg.main.deg.pers.0.2                       0.07048945
## nodefactor.deg.main.deg.pers.1.0                       0.06793310
## nodefactor.deg.main.deg.pers.1.1                       0.14121115
## nodefactor.deg.main.deg.pers.1.2                       0.13743781
## nodefactor.race..wa.B                                  0.24951222
## nodefactor.race..wa.H                                  0.23472613
## nodefactor.region.EW                                   0.22733061
## nodefactor.region.OW                                   0.35941302
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.06049134
## nodematch.race..wa.O                                   0.43120473
## absdiff.sqrt.age                                       0.42595671
##                                  nodefactor.deg.main.deg.pers.0.2
## edges                                                  0.27917599
## nodefactor.deg.main.deg.pers.0.1                       0.07048945
## nodefactor.deg.main.deg.pers.0.2                       1.00000000
## nodefactor.deg.main.deg.pers.1.0                       0.04654241
## nodefactor.deg.main.deg.pers.1.1                       0.07590497
## nodefactor.deg.main.deg.pers.1.2                       0.06752506
## nodefactor.race..wa.B                                  0.12715915
## nodefactor.race..wa.H                                  0.11535565
## nodefactor.region.EW                                   0.11160003
## nodefactor.region.OW                                   0.17732873
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.02367346
## nodematch.race..wa.O                                   0.21770820
## absdiff.sqrt.age                                       0.21585259
##                                  nodefactor.deg.main.deg.pers.1.0
## edges                                                  0.27207275
## nodefactor.deg.main.deg.pers.0.1                       0.06793310
## nodefactor.deg.main.deg.pers.0.2                       0.04654241
## nodefactor.deg.main.deg.pers.1.0                       1.00000000
## nodefactor.deg.main.deg.pers.1.1                       0.06548288
## nodefactor.deg.main.deg.pers.1.2                       0.06296366
## nodefactor.race..wa.B                                  0.09433379
## nodefactor.race..wa.H                                  0.13207342
## nodefactor.region.EW                                   0.10815694
## nodefactor.region.OW                                   0.15555399
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.03940024
## nodematch.race..wa.O                                   0.21775621
## absdiff.sqrt.age                                       0.20700790
##                                  nodefactor.deg.main.deg.pers.1.1
## edges                                                  0.49884186
## nodefactor.deg.main.deg.pers.0.1                       0.14121115
## nodefactor.deg.main.deg.pers.0.2                       0.07590497
## nodefactor.deg.main.deg.pers.1.0                       0.06548288
## nodefactor.deg.main.deg.pers.1.1                       1.00000000
## nodefactor.deg.main.deg.pers.1.2                       0.13105309
## nodefactor.race..wa.B                                  0.17348717
## nodefactor.race..wa.H                                  0.21928094
## nodefactor.region.EW                                   0.18220771
## nodefactor.region.OW                                   0.28134953
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.05192828
## nodematch.race..wa.O                                   0.41037588
## absdiff.sqrt.age                                       0.38816937
##                                  nodefactor.deg.main.deg.pers.1.2
## edges                                                  0.51465778
## nodefactor.deg.main.deg.pers.0.1                       0.13743781
## nodefactor.deg.main.deg.pers.0.2                       0.06752506
## nodefactor.deg.main.deg.pers.1.0                       0.06296366
## nodefactor.deg.main.deg.pers.1.1                       0.13105309
## nodefactor.deg.main.deg.pers.1.2                       1.00000000
## nodefactor.race..wa.B                                  0.19303486
## nodefactor.race..wa.H                                  0.26624389
## nodefactor.region.EW                                   0.21341463
## nodefactor.region.OW                                   0.31495122
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.08450180
## nodematch.race..wa.O                                   0.39531989
## absdiff.sqrt.age                                       0.39238369
##                                  nodefactor.race..wa.B
## edges                                      0.409324211
## nodefactor.deg.main.deg.pers.0.1           0.249512217
## nodefactor.deg.main.deg.pers.0.2           0.127159147
## nodefactor.deg.main.deg.pers.1.0           0.094333793
## nodefactor.deg.main.deg.pers.1.1           0.173487166
## nodefactor.deg.main.deg.pers.1.2           0.193034859
## nodefactor.race..wa.B                      1.000000000
## nodefactor.race..wa.H                     -0.000790199
## nodefactor.region.EW                       0.095902868
## nodefactor.region.OW                       0.244708877
## nodematch.race..wa.B                                NA
## nodematch.race..wa.H                       0.008461564
## nodematch.race..wa.O                      -0.003976878
## absdiff.sqrt.age                           0.314201029
##                                  nodefactor.race..wa.H
## edges                                      0.446480720
## nodefactor.deg.main.deg.pers.0.1           0.234726128
## nodefactor.deg.main.deg.pers.0.2           0.115355649
## nodefactor.deg.main.deg.pers.1.0           0.132073422
## nodefactor.deg.main.deg.pers.1.1           0.219280936
## nodefactor.deg.main.deg.pers.1.2           0.266243889
## nodefactor.race..wa.B                     -0.000790199
## nodefactor.race..wa.H                      1.000000000
## nodefactor.region.EW                       0.292086365
## nodefactor.region.OW                       0.270224399
## nodematch.race..wa.B                                NA
## nodematch.race..wa.H                       0.495077649
## nodematch.race..wa.O                       0.006256945
## absdiff.sqrt.age                           0.341708946
##                                  nodefactor.region.EW nodefactor.region.OW
## edges                                      0.40284961           0.63759906
## nodefactor.deg.main.deg.pers.0.1           0.22733061           0.35941302
## nodefactor.deg.main.deg.pers.0.2           0.11160003           0.17732873
## nodefactor.deg.main.deg.pers.1.0           0.10815694           0.15555399
## nodefactor.deg.main.deg.pers.1.1           0.18220771           0.28134953
## nodefactor.deg.main.deg.pers.1.2           0.21341463           0.31495122
## nodefactor.race..wa.B                      0.09590287           0.24470888
## nodefactor.race..wa.H                      0.29208637           0.27022440
## nodefactor.region.EW                       1.00000000           0.13056121
## nodefactor.region.OW                       0.13056121           1.00000000
## nodematch.race..wa.B                               NA                   NA
## nodematch.race..wa.H                       0.11268539           0.07293986
## nodematch.race..wa.O                       0.29230264           0.51994413
## absdiff.sqrt.age                           0.30563097           0.49312397
##                                  nodematch.race..wa.B nodematch.race..wa.H
## edges                                              NA          0.123843703
## nodefactor.deg.main.deg.pers.0.1                   NA          0.060491335
## nodefactor.deg.main.deg.pers.0.2                   NA          0.023673462
## nodefactor.deg.main.deg.pers.1.0                   NA          0.039400238
## nodefactor.deg.main.deg.pers.1.1                   NA          0.051928279
## nodefactor.deg.main.deg.pers.1.2                   NA          0.084501802
## nodefactor.race..wa.B                              NA          0.008461564
## nodefactor.race..wa.H                              NA          0.495077649
## nodefactor.region.EW                               NA          0.112685391
## nodefactor.region.OW                               NA          0.072939862
## nodematch.race..wa.B                                1                   NA
## nodematch.race..wa.H                               NA          1.000000000
## nodematch.race..wa.O                               NA         -0.003882979
## absdiff.sqrt.age                                   NA          0.086745194
##                                  nodematch.race..wa.O absdiff.sqrt.age
## edges                                     0.789036404       0.76716437
## nodefactor.deg.main.deg.pers.0.1          0.431204727       0.42595671
## nodefactor.deg.main.deg.pers.0.2          0.217708198       0.21585259
## nodefactor.deg.main.deg.pers.1.0          0.217756213       0.20700790
## nodefactor.deg.main.deg.pers.1.1          0.410375881       0.38816937
## nodefactor.deg.main.deg.pers.1.2          0.395319890       0.39238369
## nodefactor.race..wa.B                    -0.003976878       0.31420103
## nodefactor.race..wa.H                     0.006256945       0.34170895
## nodefactor.region.EW                      0.292302636       0.30563097
## nodefactor.region.OW                      0.519944130       0.49312397
## nodematch.race..wa.B                               NA               NA
## nodematch.race..wa.H                     -0.003882979       0.08674519
## nodematch.race..wa.O                      1.000000000       0.60442090
## absdiff.sqrt.age                          0.604420901       1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.007056281                     -0.002785231
## Lag 2e+05 -0.021135262                     -0.013152720
## Lag 3e+05  0.025422518                     -0.014344615
## Lag 4e+05  0.010293741                     -0.024978414
## Lag 5e+05  0.027377325                      0.023155697
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.000265926
## Lag 2e+05                     -0.011634818
## Lag 3e+05                      0.002687452
## Lag 4e+05                     -0.011767352
## Lag 5e+05                      0.000633360
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.017490733
## Lag 2e+05                     -0.004581797
## Lag 3e+05                      0.011310593
## Lag 4e+05                      0.028462300
## Lag 5e+05                     -0.012748426
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.012134856
## Lag 2e+05                     -0.011723910
## Lag 3e+05                     -0.014322647
## Lag 4e+05                     -0.001840124
## Lag 5e+05                     -0.005448570
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                         1.0000000000           1.000000000
## Lag 1e+05                     0.0289584318          -0.002959644
## Lag 2e+05                     0.0104946696          -0.023447098
## Lag 3e+05                     0.0050468198           0.005869192
## Lag 4e+05                    -0.0005164998           0.026338513
## Lag 5e+05                     0.0299984175           0.014288048
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000           1.00000000
## Lag 1e+05          -0.021150237          0.003605998           0.00660325
## Lag 2e+05          -0.022351367         -0.029485699          -0.01712849
## Lag 3e+05           0.011731415          0.034368607           0.01934329
## Lag 4e+05           0.015150003         -0.022074196          -0.00183978
## Lag 5e+05           0.001786736          0.010480002           0.02885417
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.027902380          0.021164667
## Lag 2e+05                  NaN          0.008924406         -0.005366982
## Lag 3e+05                  NaN          0.029479542          0.021864756
## Lag 4e+05                  NaN         -0.009325174          0.013878885
## Lag 5e+05                  NaN         -0.014867966          0.019561425
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.002029149
## Lag 2e+05     -0.018664590
## Lag 3e+05      0.012974076
## Lag 4e+05      0.039865232
## Lag 5e+05      0.046059834
## Chain 2 
##                 edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.00000000                      1.000000000
## Lag 1e+05  0.01213031                      0.016310015
## Lag 2e+05  0.01858269                      0.005339163
## Lag 3e+05 -0.01094819                     -0.006916254
## Lag 4e+05  0.01638422                      0.040457066
## Lag 5e+05  0.02707308                      0.017786115
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.013371001
## Lag 2e+05                     -0.002777669
## Lag 3e+05                      0.015754713
## Lag 4e+05                      0.016071698
## Lag 5e+05                     -0.029246554
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.028216429
## Lag 2e+05                      0.002224599
## Lag 3e+05                     -0.019195511
## Lag 4e+05                      0.003584746
## Lag 5e+05                      0.008798429
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.001963151
## Lag 2e+05                      0.006739325
## Lag 3e+05                     -0.001021309
## Lag 4e+05                      0.006466508
## Lag 5e+05                      0.033441550
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                     -0.015258568          -0.005245486
## Lag 2e+05                     -0.019522991          -0.004154377
## Lag 3e+05                     -0.019743377           0.020572493
## Lag 4e+05                     -0.008525181           0.005177351
## Lag 5e+05                      0.026378243          -0.024835528
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000          1.000000000
## Lag 1e+05         -0.0091514729         -0.008486691         -0.004004691
## Lag 2e+05          0.0106896118         -0.002338231          0.010767132
## Lag 3e+05         -0.0068863199          0.012321873         -0.001338476
## Lag 4e+05         -0.0005001239          0.028064680          0.014052673
## Lag 5e+05          0.0035271383          0.008949685         -0.015200181
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.001862073          0.001817307
## Lag 2e+05                  NaN         -0.028072237          0.032778775
## Lag 3e+05                  NaN          0.006960506         -0.027174320
## Lag 4e+05                  NaN         -0.004372304          0.007466263
## Lag 5e+05                  NaN         -0.015003321          0.003986580
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05      0.025845116
## Lag 2e+05      0.022889168
## Lag 3e+05     -0.016400514
## Lag 4e+05      0.005056477
## Lag 5e+05      0.026331855
## Chain 3 
##                 edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.00000000                      1.000000000
## Lag 1e+05 -0.02644799                      0.016162996
## Lag 2e+05 -0.01672671                     -0.014496743
## Lag 3e+05 -0.01465317                     -0.015783141
## Lag 4e+05 -0.01954231                      0.007744968
## Lag 5e+05 -0.03875416                     -0.002296934
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0406229061
## Lag 2e+05                     0.0009988785
## Lag 3e+05                    -0.0102288318
## Lag 4e+05                    -0.0022485793
## Lag 5e+05                     0.0096964639
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.001247232
## Lag 2e+05                     -0.011077281
## Lag 3e+05                     -0.004874879
## Lag 4e+05                      0.043938182
## Lag 5e+05                      0.012958581
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.009755835
## Lag 2e+05                      0.022813442
## Lag 3e+05                     -0.026204027
## Lag 4e+05                     -0.022936040
## Lag 5e+05                      0.005049265
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000            1.00000000
## Lag 1e+05                     -0.032813187            0.01130550
## Lag 2e+05                      0.006394019            0.01336153
## Lag 3e+05                     -0.026450550           -0.02075190
## Lag 4e+05                      0.018984957            0.01599340
## Lag 5e+05                     -0.010069015           -0.03595689
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05          -0.001475685        -0.0263703870         -0.045834960
## Lag 2e+05          -0.037526424         0.0006414004         -0.017826033
## Lag 3e+05           0.025882859        -0.0090626285         -0.020248780
## Lag 4e+05          -0.006698309        -0.0071757482         -0.015700931
## Lag 5e+05          -0.009709220        -0.0074316234         -0.008206175
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.011207233         -0.015918620
## Lag 2e+05                  NaN         -0.029379436          0.004717991
## Lag 3e+05                  NaN          0.005213389         -0.013939879
## Lag 4e+05                  NaN          0.015404411         -0.023393979
## Lag 5e+05                  NaN          0.014494502         -0.028218686
##           absdiff.sqrt.age
## Lag 0         1.0000000000
## Lag 1e+05    -0.0127242045
## Lag 2e+05     0.0003179894
## Lag 3e+05    -0.0185138217
## Lag 4e+05    -0.0212854901
## Lag 5e+05    -0.0343258898
## Chain 4 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05 -0.016570088                      0.015945491
## Lag 2e+05  0.014360505                      0.001957823
## Lag 3e+05 -0.030260908                     -0.021616788
## Lag 4e+05  0.008793576                     -0.016943128
## Lag 5e+05 -0.001246098                      0.002499013
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                     -0.005908801
## Lag 2e+05                     -0.005642780
## Lag 3e+05                      0.024266817
## Lag 4e+05                     -0.006894492
## Lag 5e+05                      0.004289095
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.001517593
## Lag 2e+05                     -0.002616586
## Lag 3e+05                     -0.006004276
## Lag 4e+05                     -0.003205876
## Lag 5e+05                     -0.006425879
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.019262737
## Lag 2e+05                     -0.009717663
## Lag 3e+05                     -0.020157961
## Lag 4e+05                     -0.023209813
## Lag 5e+05                     -0.005703401
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                     -0.014601731           0.003861248
## Lag 2e+05                      0.005563967           0.002024204
## Lag 3e+05                      0.010299065          -0.032901948
## Lag 4e+05                      0.002860086           0.003664151
## Lag 5e+05                      0.004961345           0.007383125
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000          1.000000000         1.0000000000
## Lag 1e+05           -0.00324699          0.002696443        -0.0034260126
## Lag 2e+05           -0.01024943          0.018962006        -0.0002813096
## Lag 3e+05           -0.01506509         -0.016139609        -0.0245294187
## Lag 4e+05            0.02768955         -0.001003349         0.0110070450
## Lag 5e+05           -0.01710934         -0.035956990        -0.0045496472
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000         1.0000000000
## Lag 1e+05                  NaN          0.011714703        -0.0072573192
## Lag 2e+05                  NaN         -0.008738242         0.0032981640
## Lag 3e+05                  NaN         -0.005436846        -0.0101228962
## Lag 4e+05                  NaN          0.019321869         0.0002283467
## Lag 5e+05                  NaN          0.014063531         0.0036617345
##           absdiff.sqrt.age
## Lag 0           1.00000000
## Lag 1e+05      -0.01961981
## Lag 2e+05       0.01574212
## Lag 3e+05      -0.01365908
## Lag 4e+05       0.00246615
## Lag 5e+05      -0.01317773
## Chain 5 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05 -0.027214559                     -0.016299436
## Lag 2e+05 -0.021741841                      0.001471485
## Lag 3e+05  0.014918050                      0.011430937
## Lag 4e+05 -0.006266827                      0.027655925
## Lag 5e+05 -0.013643230                      0.003657690
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.013000534
## Lag 2e+05                     -0.013564773
## Lag 3e+05                     -0.003317423
## Lag 4e+05                     -0.002816633
## Lag 5e+05                     -0.008108482
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.002923422
## Lag 2e+05                     -0.006552494
## Lag 3e+05                      0.025286439
## Lag 4e+05                      0.005182189
## Lag 5e+05                      0.001075252
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.033048374
## Lag 2e+05                     -0.001446418
## Lag 3e+05                      0.001886635
## Lag 4e+05                      0.011924843
## Lag 5e+05                     -0.017818677
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000          1.0000000000
## Lag 1e+05                      0.002004997         -0.0009264303
## Lag 2e+05                     -0.024401575          0.0115482931
## Lag 3e+05                      0.030378981          0.0041296285
## Lag 4e+05                     -0.015414178          0.0171065331
## Lag 5e+05                     -0.002458135          0.0066072500
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.013458240          0.008928397          0.001228152
## Lag 2e+05          -0.022165628          0.015741361          0.014265919
## Lag 3e+05          -0.012385518          0.003600822          0.027370289
## Lag 4e+05           0.001907997         -0.004464104         -0.007145470
## Lag 5e+05           0.036073441         -0.004034306          0.011613905
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.010957694         -0.020134092
## Lag 2e+05                  NaN         -0.021769598         -0.024481115
## Lag 3e+05                  NaN          0.005029376          0.020082841
## Lag 4e+05                  NaN          0.022086437         -0.009264074
## Lag 5e+05                  NaN         -0.013009168         -0.023829157
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.004417282
## Lag 2e+05     -0.014015133
## Lag 3e+05      0.003266299
## Lag 4e+05     -0.002614453
## Lag 5e+05     -0.010438971
## Chain 6 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05 -0.019780631                     -0.001570657
## Lag 2e+05  0.016469165                     -0.029966847
## Lag 3e+05 -0.022651521                      0.002728267
## Lag 4e+05  0.001078758                      0.017036899
## Lag 5e+05  0.024601159                     -0.007310330
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.000000e+00
## Lag 1e+05                     2.094967e-03
## Lag 2e+05                     2.273921e-02
## Lag 3e+05                    -7.698009e-05
## Lag 4e+05                     1.048373e-03
## Lag 5e+05                    -6.505166e-03
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0134056198
## Lag 2e+05                    -0.0370667933
## Lag 3e+05                     0.0008083579
## Lag 4e+05                    -0.0058882589
## Lag 5e+05                     0.0041203251
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.010295887
## Lag 2e+05                      0.002661322
## Lag 3e+05                      0.002000594
## Lag 4e+05                      0.005341205
## Lag 5e+05                     -0.007333352
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                     -0.008343426           0.010347760
## Lag 2e+05                     -0.010871633          -0.003184313
## Lag 3e+05                      0.026591831           0.006953221
## Lag 4e+05                     -0.021068282           0.018230890
## Lag 5e+05                      0.005842443           0.017916683
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05          -0.001500148         0.0046034204         -0.010017978
## Lag 2e+05          -0.021456673        -0.0046246945         -0.003590417
## Lag 3e+05          -0.028586022        -0.0128619889         -0.018079315
## Lag 4e+05          -0.003830996         0.0029357498          0.009581578
## Lag 5e+05           0.012693663         0.0007158349          0.011783370
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.025873051         -0.019946768
## Lag 2e+05                  NaN         -0.023348388          0.023800365
## Lag 3e+05                  NaN         -0.012113476         -0.008591662
## Lag 4e+05                  NaN          0.006379161         -0.009732338
## Lag 5e+05                  NaN          0.013105356          0.005112996
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05      0.002827015
## Lag 2e+05      0.025214083
## Lag 3e+05     -0.031187960
## Lag 4e+05      0.015982239
## Lag 5e+05      0.003723767
## Chain 7 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05 -0.002944805                     -0.034033505
## Lag 2e+05  0.014668325                     -0.009302330
## Lag 3e+05  0.014794588                      0.001528488
## Lag 4e+05  0.005474308                     -0.015706194
## Lag 5e+05  0.018940823                     -0.004087485
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.002854076
## Lag 2e+05                     -0.011935902
## Lag 3e+05                     -0.017846142
## Lag 4e+05                      0.008206171
## Lag 5e+05                     -0.017991951
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0053036471
## Lag 2e+05                     0.0222473725
## Lag 3e+05                    -0.0081169843
## Lag 4e+05                     0.0009750732
## Lag 5e+05                    -0.0005359841
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.022235870
## Lag 2e+05                      0.024111286
## Lag 3e+05                     -0.014550622
## Lag 4e+05                     -0.002278935
## Lag 5e+05                     -0.001677464
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                         1.0000000000           1.000000000
## Lag 1e+05                     0.0040941145           0.019906863
## Lag 2e+05                    -0.0076752089          -0.032385654
## Lag 3e+05                    -0.0092861156          -0.015088017
## Lag 4e+05                     0.0062243455           0.001792484
## Lag 5e+05                     0.0001165649           0.016404863
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000          1.000000000
## Lag 1e+05          0.0111806892          0.006029104          0.015270578
## Lag 2e+05          0.0002266331         -0.005595261          0.006745850
## Lag 3e+05          0.0077403678          0.009918769          0.006145884
## Lag 4e+05          0.0099564181          0.013915667         -0.020881549
## Lag 5e+05         -0.0267981464          0.032547415          0.001098759
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.005012424          0.016881636
## Lag 2e+05                  NaN          0.047344958          0.011157016
## Lag 3e+05                  NaN          0.012164671          0.003598625
## Lag 4e+05                  NaN          0.006828074         -0.010545739
## Lag 5e+05                  NaN         -0.014594779          0.014376288
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.017739504
## Lag 2e+05     -0.013718393
## Lag 3e+05      0.011196826
## Lag 4e+05      0.008692134
## Lag 5e+05      0.005013385
## Chain 8 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05 -0.014970724                      0.005200106
## Lag 2e+05  0.013653682                     -0.014489236
## Lag 3e+05 -0.027917714                     -0.005196370
## Lag 4e+05  0.023148084                      0.017432030
## Lag 5e+05 -0.004880321                     -0.014370787
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0292102200
## Lag 2e+05                    -0.0269437139
## Lag 3e+05                     0.0096498356
## Lag 4e+05                     0.0285146194
## Lag 5e+05                    -0.0007426275
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.009659831
## Lag 2e+05                      0.001713837
## Lag 3e+05                     -0.002907522
## Lag 4e+05                      0.033835251
## Lag 5e+05                     -0.024026960
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.018310956
## Lag 2e+05                      0.002909554
## Lag 3e+05                     -0.014039928
## Lag 4e+05                      0.015339893
## Lag 5e+05                     -0.005377928
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000          1.000000e+00
## Lag 1e+05                     -0.002779993          2.677834e-03
## Lag 2e+05                      0.018298059          1.502947e-02
## Lag 3e+05                     -0.005165912         -1.260458e-02
## Lag 4e+05                      0.018797727         -7.672364e-05
## Lag 5e+05                      0.016490648         -3.258503e-03
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05          -0.006101122           0.01155198          0.000251798
## Lag 2e+05          -0.028469288           0.01275553          0.011561489
## Lag 3e+05          -0.019608896          -0.02053795         -0.017322908
## Lag 4e+05          -0.025961959           0.01234252          0.002567985
## Lag 5e+05          -0.012568898          -0.01784316          0.013632498
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.003236059         -0.025513899
## Lag 2e+05                  NaN          0.002812923          0.012069739
## Lag 3e+05                  NaN         -0.020704887         -0.006163815
## Lag 4e+05                  NaN         -0.047269104          0.003075766
## Lag 5e+05                  NaN          0.048069886          0.024196928
##           absdiff.sqrt.age
## Lag 0         1.0000000000
## Lag 1e+05    -0.0059556220
## Lag 2e+05     0.0076051272
## Lag 3e+05    -0.0090232706
## Lag 4e+05     0.0336374020
## Lag 5e+05    -0.0008586777
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.72378                         -0.11626 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.09011                         -0.67347 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.69219                         -0.24630 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          0.29340                          0.52136 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          0.80635                          0.62426 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                         -0.82516 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                          0.32264                          0.60639 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.4692007                        0.9074469 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.9282031                        0.5006509 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.4888156                        0.8054481 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.7692193                        0.6021130 
##             nodefactor.region.EW             nodefactor.region.OW 
##                        0.4200434                        0.5324550 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                        0.4092793 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                        0.7469681                        0.5442531 
## Joint P-value (lower = worse):  0.9744758 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -0.32807                          0.67497 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -1.35634                         -1.52446 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.09233                         -0.45062 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                         -0.14301                          1.23381 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          0.13237                         -0.61665 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                          0.04806 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                         -1.15238                         -1.01498 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.7428608                        0.4996954 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.1749912                        0.1273947 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.9264374                        0.6522655 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.8862842                        0.2172735 
##             nodefactor.region.EW             nodefactor.region.OW 
##                        0.8946911                        0.5374663 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                        0.9616703 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                        0.2491665                        0.3101168 
## Joint P-value (lower = worse):  0.7277695 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -0.02761                          1.32866 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          0.20200                         -1.12687 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.33890                         -0.49716 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          1.18312                         -0.47284 
##             nodefactor.region.EW             nodefactor.region.OW 
##                         -0.79236                          0.48598 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                         -1.20431 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                         -0.54239                          0.67171 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.9779753                        0.1839585 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.8399179                        0.2597964 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.7346874                        0.6190772 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.2367597                        0.6363302 
##             nodefactor.region.EW             nodefactor.region.OW 
##                        0.4281516                        0.6269832 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                        0.2284679 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                        0.5875510                        0.5017652 
## Joint P-value (lower = worse):  0.852875 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           0.7533                           0.4672 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          -0.1330                           1.0433 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          -0.5375                           0.3701 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                           0.3134                          -0.3495 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          -2.4509                           0.3408 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                           0.2091 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                           1.0046                           1.4842 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.45125794                       0.64033339 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.89415804                       0.29682336 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.59089274                       0.71127251 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.75399822                       0.72674246 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.01424804                       0.73326850 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.83438877 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                       0.31506754                       0.13774303 
## Joint P-value (lower = worse):  0.5030046 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.82133                          0.35860 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.05003                          0.18208 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.34493                          0.89177 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                         -0.64871                          0.15472 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          0.38242                         -0.63965 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                          0.37437 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                          1.31588                         -0.05213 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.4114597                        0.7198928 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.9600974                        0.8555197 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.7301477                        0.3725165 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.5165259                        0.8770436 
##             nodefactor.region.EW             nodefactor.region.OW 
##                        0.7021472                        0.5224014 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                        0.7081326 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                        0.1882154                        0.9584237 
## Joint P-value (lower = worse):  0.9211648 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.85085                         -0.38745 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.61558                          1.60687 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.70918                          1.23653 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          1.40982                          0.41193 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          0.72537                         -0.03523 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                         -1.41312 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                         -0.06633                          0.85788 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.3948522                        0.6984216 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.5381744                        0.1080830 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.4782104                        0.2162605 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.1585936                        0.6803932 
##             nodefactor.region.EW             nodefactor.region.OW 
##                        0.4682245                        0.9719003 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                        0.1576208 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                        0.9471147                        0.3909578 
## Joint P-value (lower = worse):  0.6706843 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          -0.3153                           0.1738 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          -1.5981                           0.6326 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          -0.7857                           1.7644 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          -2.0396                          -0.3636 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          -1.0007                          -0.7458 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                           1.1230 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                           1.0895                          -0.4911 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.75251933                       0.86205854 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.11001215                       0.52699791 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.43202067                       0.07766762 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.04138605                       0.71612360 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.31696941                       0.45576287 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.26143342 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                       0.27592923                       0.62334366 
## Joint P-value (lower = worse):  0.1881114 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -0.06097                          1.00028 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.89386                         -0.61363 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.09069                          0.12623 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                         -1.72746                         -0.12068 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          1.28776                          1.18485 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                         -0.74948 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                          0.76758                         -0.15436 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.95138596                       0.31717573 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.37139713                       0.53945991 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.92773836                       0.89955372 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.08408497                       0.90394606 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.19782832                       0.23607743 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                              NaN                       0.45356593 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                       0.44273884                       0.87732407 
## Joint P-value (lower = worse):  0.5679515 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 7

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                                      Mean     SD Naive SE Time-series SE
## edges                            -2.78043 21.924  0.12658        0.14990
## nodefactor.deg.main.deg.pers.0.1  0.57943 14.280  0.08245        0.11037
## nodefactor.deg.main.deg.pers.0.2  0.02853  6.256  0.03612        0.03653
## nodefactor.deg.main.deg.pers.1.0  0.06819  6.160  0.03557        0.03557
## nodefactor.deg.main.deg.pers.1.1  0.11538 12.395  0.07156        0.09681
## nodefactor.deg.main.deg.pers.1.2  0.35236 13.016  0.07515        0.09403
## nodefactor.riskg.O2              -0.40092  0.000  0.00000        0.00000
## nodefactor.riskg.O3              -1.44772  2.341  0.01352        0.01348
## nodefactor.riskg.O4              -1.43580 11.546  0.06666        0.06963
## nodefactor.riskg.Y1              -1.34908  0.000  0.00000        0.00000
## nodefactor.riskg.Y2              -1.49884  2.609  0.01506        0.01501
## nodefactor.riskg.Y3              -1.44773  8.571  0.04948        0.04941
## nodefactor.riskg.Y4              -0.07479 37.320  0.21547        0.26598
## nodefactor.race..wa.B             6.53157  9.080  0.05242        0.06894
## nodefactor.race..wa.H             6.38132 11.003  0.06353        0.07767
## nodefactor.region.EW             -0.04259  9.554  0.05516        0.06126
## nodefactor.region.OW              0.80288 17.351  0.10018        0.10658
## nodematch.race..wa.B             -2.53754  0.000  0.00000        0.00000
## nodematch.race..wa.H             -6.38123  2.628  0.01518        0.01958
## nodematch.race..wa.O              6.80672 17.220  0.09942        0.11307
## absdiff.sqrt.age                  0.25493 22.438  0.12955        0.13870
## 
## 2. Quantiles for each variable:
## 
##                                      2.5%      25%      50%     75%
## edges                            -45.5122 -17.5122 -2.51216 11.4878
## nodefactor.deg.main.deg.pers.0.1 -27.1754  -9.1754  0.82459  9.8246
## nodefactor.deg.main.deg.pers.0.2 -11.2419  -4.2169 -0.21687  3.7831
## nodefactor.deg.main.deg.pers.1.0 -11.5677  -4.5677 -0.56768  4.4323
## nodefactor.deg.main.deg.pers.1.1 -23.3643  -8.3643 -0.36432  8.6357
## nodefactor.deg.main.deg.pers.1.2 -24.8703  -8.8703  0.12966  9.1297
## nodefactor.riskg.O2               -0.4009  -0.4009 -0.40092 -0.4009
## nodefactor.riskg.O3               -5.8558  -2.8558 -1.85582  0.1442
## nodefactor.riskg.O4              -23.5127  -9.5127 -1.51266  6.4873
## nodefactor.riskg.Y1               -1.3491  -1.3491 -1.34908 -1.3491
## nodefactor.riskg.Y2               -6.2024  -3.2024 -1.20238 -0.2024
## nodefactor.riskg.Y3              -17.7860  -7.7860 -1.78597  4.2140
## nodefactor.riskg.Y4              -73.0115 -25.0115 -0.01152 24.9885
## nodefactor.race..wa.B            -11.1865  -0.1865  6.81350 12.8135
## nodefactor.race..wa.H            -14.8353  -0.8353  6.16472 14.1647
## nodefactor.region.EW             -18.3887  -6.3887 -0.38872  6.6113
## nodefactor.region.OW             -32.1591 -11.1591  0.84094 11.8409
## nodematch.race..wa.B              -2.5375  -2.5375 -2.53754 -2.5375
## nodematch.race..wa.H             -11.2750  -8.2750 -6.27496 -4.2750
## nodematch.race..wa.O             -26.8841  -4.8841  7.11592 18.1159
## absdiff.sqrt.age                 -42.9408 -14.8934 -0.06949 15.3776
##                                    97.5%
## edges                            40.4878
## nodefactor.deg.main.deg.pers.0.1 28.8246
## nodefactor.deg.main.deg.pers.0.2 12.7831
## nodefactor.deg.main.deg.pers.1.0 12.4323
## nodefactor.deg.main.deg.pers.1.1 25.6357
## nodefactor.deg.main.deg.pers.1.2 26.1297
## nodefactor.riskg.O2              -0.4009
## nodefactor.riskg.O3               3.1442
## nodefactor.riskg.O4              21.4873
## nodefactor.riskg.Y1              -1.3491
## nodefactor.riskg.Y2               3.7976
## nodefactor.riskg.Y3              16.2140
## nodefactor.riskg.Y4              72.9885
## nodefactor.race..wa.B            24.8135
## nodefactor.race..wa.H            28.1647
## nodefactor.region.EW             19.6113
## nodefactor.region.OW             35.8409
## nodematch.race..wa.B             -2.5375
## nodematch.race..wa.H             -1.2750
## nodematch.race..wa.O             41.1159
## absdiff.sqrt.age                 45.0626
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                                      edges
## edges                            1.0000000
## nodefactor.deg.main.deg.pers.0.1 0.5605254
## nodefactor.deg.main.deg.pers.0.2 0.2739066
## nodefactor.deg.main.deg.pers.1.0 0.2740702
## nodefactor.deg.main.deg.pers.1.1 0.4970060
## nodefactor.deg.main.deg.pers.1.2 0.5197052
## nodefactor.riskg.O2                     NA
## nodefactor.riskg.O3              0.1007890
## nodefactor.riskg.O4              0.4431162
## nodefactor.riskg.Y1                     NA
## nodefactor.riskg.Y2              0.1301309
## nodefactor.riskg.Y3              0.3601217
## nodefactor.riskg.Y4              0.9396998
## nodefactor.race..wa.B            0.4117198
## nodefactor.race..wa.H            0.4498201
## nodefactor.region.EW             0.4049308
## nodefactor.region.OW             0.6340514
## nodematch.race..wa.B                    NA
## nodematch.race..wa.H             0.1219231
## nodematch.race..wa.O             0.7872474
## absdiff.sqrt.age                 0.7777615
##                                  nodefactor.deg.main.deg.pers.0.1
## edges                                                  0.56052539
## nodefactor.deg.main.deg.pers.0.1                       1.00000000
## nodefactor.deg.main.deg.pers.0.2                       0.07454453
## nodefactor.deg.main.deg.pers.1.0                       0.08588406
## nodefactor.deg.main.deg.pers.1.1                       0.13951861
## nodefactor.deg.main.deg.pers.1.2                       0.14556992
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.05660025
## nodefactor.riskg.O4                                    0.25685686
## nodefactor.riskg.Y1                                            NA
## nodefactor.riskg.Y2                                    0.06280708
## nodefactor.riskg.Y3                                    0.19486200
## nodefactor.riskg.Y4                                    0.52641072
## nodefactor.race..wa.B                                  0.22523125
## nodefactor.race..wa.H                                  0.20850976
## nodefactor.region.EW                                   0.21549292
## nodefactor.region.OW                                   0.34369663
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.05045618
## nodematch.race..wa.O                                   0.46934321
## absdiff.sqrt.age                                       0.44714061
##                                  nodefactor.deg.main.deg.pers.0.2
## edges                                                  0.27390664
## nodefactor.deg.main.deg.pers.0.1                       0.07454453
## nodefactor.deg.main.deg.pers.0.2                       1.00000000
## nodefactor.deg.main.deg.pers.1.0                       0.03129919
## nodefactor.deg.main.deg.pers.1.1                       0.07278331
## nodefactor.deg.main.deg.pers.1.2                       0.07141051
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.04230945
## nodefactor.riskg.O4                                    0.10481873
## nodefactor.riskg.Y1                                            NA
## nodefactor.riskg.Y2                                    0.03173576
## nodefactor.riskg.Y3                                    0.09184978
## nodefactor.riskg.Y4                                    0.26342180
## nodefactor.race..wa.B                                  0.10940619
## nodefactor.race..wa.H                                  0.11784089
## nodefactor.region.EW                                   0.10227189
## nodefactor.region.OW                                   0.18720337
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.03540397
## nodematch.race..wa.O                                   0.22114384
## absdiff.sqrt.age                                       0.21524177
##                                  nodefactor.deg.main.deg.pers.1.0
## edges                                                  0.27407021
## nodefactor.deg.main.deg.pers.0.1                       0.08588406
## nodefactor.deg.main.deg.pers.0.2                       0.03129919
## nodefactor.deg.main.deg.pers.1.0                       1.00000000
## nodefactor.deg.main.deg.pers.1.1                       0.06600732
## nodefactor.deg.main.deg.pers.1.2                       0.06978487
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.02584123
## nodefactor.riskg.O4                                    0.13229956
## nodefactor.riskg.Y1                                            NA
## nodefactor.riskg.Y2                                    0.04362079
## nodefactor.riskg.Y3                                    0.09964898
## nodefactor.riskg.Y4                                    0.25352273
## nodefactor.race..wa.B                                  0.09673413
## nodefactor.race..wa.H                                  0.13340136
## nodefactor.region.EW                                   0.11971967
## nodefactor.region.OW                                   0.15902807
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.02950182
## nodematch.race..wa.O                                   0.21719009
## absdiff.sqrt.age                                       0.21741229
##                                  nodefactor.deg.main.deg.pers.1.1
## edges                                                  0.49700599
## nodefactor.deg.main.deg.pers.0.1                       0.13951861
## nodefactor.deg.main.deg.pers.0.2                       0.07278331
## nodefactor.deg.main.deg.pers.1.0                       0.06600732
## nodefactor.deg.main.deg.pers.1.1                       1.00000000
## nodefactor.deg.main.deg.pers.1.2                       0.13017730
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.05598420
## nodefactor.riskg.O4                                    0.23702949
## nodefactor.riskg.Y1                                            NA
## nodefactor.riskg.Y2                                    0.07362575
## nodefactor.riskg.Y3                                    0.19148955
## nodefactor.riskg.Y4                                    0.45797171
## nodefactor.race..wa.B                                  0.16723786
## nodefactor.race..wa.H                                  0.22908921
## nodefactor.region.EW                                   0.14899367
## nodefactor.region.OW                                   0.28156331
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.06350756
## nodematch.race..wa.O                                   0.40789437
## absdiff.sqrt.age                                       0.39219056
##                                  nodefactor.deg.main.deg.pers.1.2
## edges                                                  0.51970522
## nodefactor.deg.main.deg.pers.0.1                       0.14556992
## nodefactor.deg.main.deg.pers.0.2                       0.07141051
## nodefactor.deg.main.deg.pers.1.0                       0.06978487
## nodefactor.deg.main.deg.pers.1.1                       0.13017730
## nodefactor.deg.main.deg.pers.1.2                       1.00000000
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.04908002
## nodefactor.riskg.O4                                    0.22980741
## nodefactor.riskg.Y1                                            NA
## nodefactor.riskg.Y2                                    0.05779263
## nodefactor.riskg.Y3                                    0.18419879
## nodefactor.riskg.Y4                                    0.49009034
## nodefactor.race..wa.B                                  0.19340128
## nodefactor.race..wa.H                                  0.27115908
## nodefactor.region.EW                                   0.24797115
## nodefactor.region.OW                                   0.33426849
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.08216250
## nodematch.race..wa.O                                   0.39896472
## absdiff.sqrt.age                                       0.39361530
##                                  nodefactor.riskg.O2 nodefactor.riskg.O3
## edges                                             NA         0.100789045
## nodefactor.deg.main.deg.pers.0.1                  NA         0.056600247
## nodefactor.deg.main.deg.pers.0.2                  NA         0.042309452
## nodefactor.deg.main.deg.pers.1.0                  NA         0.025841225
## nodefactor.deg.main.deg.pers.1.1                  NA         0.055984200
## nodefactor.deg.main.deg.pers.1.2                  NA         0.049080019
## nodefactor.riskg.O2                                1                  NA
## nodefactor.riskg.O3                               NA         1.000000000
## nodefactor.riskg.O4                               NA         0.048733107
## nodefactor.riskg.Y1                               NA                  NA
## nodefactor.riskg.Y2                               NA         0.005793377
## nodefactor.riskg.Y3                               NA         0.020443779
## nodefactor.riskg.Y4                               NA         0.035508134
## nodefactor.race..wa.B                             NA         0.036353605
## nodefactor.race..wa.H                             NA         0.044670436
## nodefactor.region.EW                              NA         0.047443109
## nodefactor.region.OW                              NA         0.065192544
## nodematch.race..wa.B                              NA                  NA
## nodematch.race..wa.H                              NA         0.009761303
## nodematch.race..wa.O                              NA         0.082097910
## absdiff.sqrt.age                                  NA         0.101819078
##                                  nodefactor.riskg.O4 nodefactor.riskg.Y1
## edges                                     0.44311618                  NA
## nodefactor.deg.main.deg.pers.0.1          0.25685686                  NA
## nodefactor.deg.main.deg.pers.0.2          0.10481873                  NA
## nodefactor.deg.main.deg.pers.1.0          0.13229956                  NA
## nodefactor.deg.main.deg.pers.1.1          0.23702949                  NA
## nodefactor.deg.main.deg.pers.1.2          0.22980741                  NA
## nodefactor.riskg.O2                               NA                  NA
## nodefactor.riskg.O3                       0.04873311                  NA
## nodefactor.riskg.O4                       1.00000000                  NA
## nodefactor.riskg.Y1                               NA                   1
## nodefactor.riskg.Y2                       0.02647222                  NA
## nodefactor.riskg.Y3                       0.07434586                  NA
## nodefactor.riskg.Y4                       0.18925336                  NA
## nodefactor.race..wa.B                     0.16702725                  NA
## nodefactor.race..wa.H                     0.16340843                  NA
## nodefactor.region.EW                      0.18825714                  NA
## nodefactor.region.OW                      0.27393940                  NA
## nodematch.race..wa.B                              NA                  NA
## nodematch.race..wa.H                      0.03794673                  NA
## nodematch.race..wa.O                      0.37746230                  NA
## absdiff.sqrt.age                          0.43607921                  NA
##                                  nodefactor.riskg.Y2 nodefactor.riskg.Y3
## edges                                    0.130130855          0.36012174
## nodefactor.deg.main.deg.pers.0.1         0.062807082          0.19486200
## nodefactor.deg.main.deg.pers.0.2         0.031735756          0.09184978
## nodefactor.deg.main.deg.pers.1.0         0.043620786          0.09964898
## nodefactor.deg.main.deg.pers.1.1         0.073625746          0.19148955
## nodefactor.deg.main.deg.pers.1.2         0.057792629          0.18419879
## nodefactor.riskg.O2                               NA                  NA
## nodefactor.riskg.O3                      0.005793377          0.02044378
## nodefactor.riskg.O4                      0.026472218          0.07434586
## nodefactor.riskg.Y1                               NA                  NA
## nodefactor.riskg.Y2                      1.000000000          0.02380483
## nodefactor.riskg.Y3                      0.023804828          1.00000000
## nodefactor.riskg.Y4                      0.068959172          0.16751269
## nodefactor.race..wa.B                    0.059980442          0.14184952
## nodefactor.race..wa.H                    0.048167781          0.18255512
## nodefactor.region.EW                     0.049491417          0.14858426
## nodefactor.region.OW                     0.084386499          0.23215072
## nodematch.race..wa.B                              NA                  NA
## nodematch.race..wa.H                     0.014376806          0.05370028
## nodematch.race..wa.O                     0.105466428          0.27524416
## absdiff.sqrt.age                         0.102258333          0.27654786
##                                  nodefactor.riskg.Y4 nodefactor.race..wa.B
## edges                                     0.93969982          0.4117198470
## nodefactor.deg.main.deg.pers.0.1          0.52641072          0.2252312544
## nodefactor.deg.main.deg.pers.0.2          0.26342180          0.1094061947
## nodefactor.deg.main.deg.pers.1.0          0.25352273          0.0967341318
## nodefactor.deg.main.deg.pers.1.1          0.45797171          0.1672378572
## nodefactor.deg.main.deg.pers.1.2          0.49009034          0.1934012770
## nodefactor.riskg.O2                               NA                    NA
## nodefactor.riskg.O3                       0.03550813          0.0363536050
## nodefactor.riskg.O4                       0.18925336          0.1670272532
## nodefactor.riskg.Y1                               NA                    NA
## nodefactor.riskg.Y2                       0.06895917          0.0599804424
## nodefactor.riskg.Y3                       0.16751269          0.1418495154
## nodefactor.riskg.Y4                       1.00000000          0.3930108559
## nodefactor.race..wa.B                     0.39301086          1.0000000000
## nodefactor.race..wa.H                     0.42985122         -0.0008758497
## nodefactor.region.EW                      0.37695689          0.1011031305
## nodefactor.region.OW                      0.59690150          0.2474581662
## nodematch.race..wa.B                              NA                    NA
## nodematch.race..wa.H                      0.11755962         -0.0025292097
## nodematch.race..wa.O                      0.73243463         -0.0029159437
## absdiff.sqrt.age                          0.70184169          0.3190775130
##                                  nodefactor.race..wa.H
## edges                                     0.4498201209
## nodefactor.deg.main.deg.pers.0.1          0.2085097615
## nodefactor.deg.main.deg.pers.0.2          0.1178408947
## nodefactor.deg.main.deg.pers.1.0          0.1334013569
## nodefactor.deg.main.deg.pers.1.1          0.2290892071
## nodefactor.deg.main.deg.pers.1.2          0.2711590810
## nodefactor.riskg.O2                                 NA
## nodefactor.riskg.O3                       0.0446704356
## nodefactor.riskg.O4                       0.1634084264
## nodefactor.riskg.Y1                                 NA
## nodefactor.riskg.Y2                       0.0481677806
## nodefactor.riskg.Y3                       0.1825551171
## nodefactor.riskg.Y4                       0.4298512178
## nodefactor.race..wa.B                    -0.0008758497
## nodefactor.race..wa.H                     1.0000000000
## nodefactor.region.EW                      0.2847026380
## nodefactor.region.OW                      0.2982866880
## nodematch.race..wa.B                                NA
## nodematch.race..wa.H                      0.4771378032
## nodematch.race..wa.O                      0.0070082673
## absdiff.sqrt.age                          0.3424946071
##                                  nodefactor.region.EW nodefactor.region.OW
## edges                                      0.40493077           0.63405139
## nodefactor.deg.main.deg.pers.0.1           0.21549292           0.34369663
## nodefactor.deg.main.deg.pers.0.2           0.10227189           0.18720337
## nodefactor.deg.main.deg.pers.1.0           0.11971967           0.15902807
## nodefactor.deg.main.deg.pers.1.1           0.14899367           0.28156331
## nodefactor.deg.main.deg.pers.1.2           0.24797115           0.33426849
## nodefactor.riskg.O2                                NA                   NA
## nodefactor.riskg.O3                        0.04744311           0.06519254
## nodefactor.riskg.O4                        0.18825714           0.27393940
## nodefactor.riskg.Y1                                NA                   NA
## nodefactor.riskg.Y2                        0.04949142           0.08438650
## nodefactor.riskg.Y3                        0.14858426           0.23215072
## nodefactor.riskg.Y4                        0.37695689           0.59690150
## nodefactor.race..wa.B                      0.10110313           0.24745817
## nodefactor.race..wa.H                      0.28470264           0.29828669
## nodefactor.region.EW                       1.00000000           0.13377556
## nodefactor.region.OW                       0.13377556           1.00000000
## nodematch.race..wa.B                               NA                   NA
## nodematch.race..wa.H                       0.10136292           0.08093731
## nodematch.race..wa.O                       0.29578286           0.49852081
## absdiff.sqrt.age                           0.31029802           0.49877732
##                                  nodematch.race..wa.B nodematch.race..wa.H
## edges                                              NA          0.121923149
## nodefactor.deg.main.deg.pers.0.1                   NA          0.050456179
## nodefactor.deg.main.deg.pers.0.2                   NA          0.035403966
## nodefactor.deg.main.deg.pers.1.0                   NA          0.029501818
## nodefactor.deg.main.deg.pers.1.1                   NA          0.063507562
## nodefactor.deg.main.deg.pers.1.2                   NA          0.082162499
## nodefactor.riskg.O2                                NA                   NA
## nodefactor.riskg.O3                                NA          0.009761303
## nodefactor.riskg.O4                                NA          0.037946733
## nodefactor.riskg.Y1                                NA                   NA
## nodefactor.riskg.Y2                                NA          0.014376806
## nodefactor.riskg.Y3                                NA          0.053700281
## nodefactor.riskg.Y4                                NA          0.117559617
## nodefactor.race..wa.B                              NA         -0.002529210
## nodefactor.race..wa.H                              NA          0.477137803
## nodefactor.region.EW                               NA          0.101362915
## nodefactor.region.OW                               NA          0.080937309
## nodematch.race..wa.B                                1                   NA
## nodematch.race..wa.H                               NA          1.000000000
## nodematch.race..wa.O                               NA          0.004322449
## absdiff.sqrt.age                                   NA          0.087913161
##                                  nodematch.race..wa.O absdiff.sqrt.age
## edges                                     0.787247394       0.77776154
## nodefactor.deg.main.deg.pers.0.1          0.469343210       0.44714061
## nodefactor.deg.main.deg.pers.0.2          0.221143843       0.21524177
## nodefactor.deg.main.deg.pers.1.0          0.217190090       0.21741229
## nodefactor.deg.main.deg.pers.1.1          0.407894370       0.39219056
## nodefactor.deg.main.deg.pers.1.2          0.398964724       0.39361530
## nodefactor.riskg.O2                                NA               NA
## nodefactor.riskg.O3                       0.082097910       0.10181908
## nodefactor.riskg.O4                       0.377462296       0.43607921
## nodefactor.riskg.Y1                                NA               NA
## nodefactor.riskg.Y2                       0.105466428       0.10225833
## nodefactor.riskg.Y3                       0.275244157       0.27654786
## nodefactor.riskg.Y4                       0.732434632       0.70184169
## nodefactor.race..wa.B                    -0.002915944       0.31907751
## nodefactor.race..wa.H                     0.007008267       0.34249461
## nodefactor.region.EW                      0.295782863       0.31029802
## nodefactor.region.OW                      0.498520806       0.49877732
## nodematch.race..wa.B                               NA               NA
## nodematch.race..wa.H                      0.004322449       0.08791316
## nodematch.race..wa.O                      1.000000000       0.61653938
## absdiff.sqrt.age                          0.616539378       1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                       1.00000000
## Lag 1e+05 0.13468509                       0.23808224
## Lag 2e+05 0.06179420                       0.11862717
## Lag 3e+05 0.04815151                       0.05756046
## Lag 4e+05 0.02969748                       0.04370035
## Lag 5e+05 0.02661905                       0.01389898
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.015096844
## Lag 2e+05                      0.021169862
## Lag 3e+05                     -0.004870052
## Lag 4e+05                      0.032936814
## Lag 5e+05                      0.002277464
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.003856845
## Lag 2e+05                     -0.019514635
## Lag 3e+05                      0.010967055
## Lag 4e+05                     -0.018721342
## Lag 5e+05                     -0.023307730
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.25017721
## Lag 2e+05                       0.14068745
## Lag 3e+05                       0.08702326
## Lag 4e+05                       0.04404667
## Lag 5e+05                       0.01913111
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.18732751                 NaN
## Lag 2e+05                       0.05698054                 NaN
## Lag 3e+05                       0.01403623                 NaN
## Lag 4e+05                       0.02084029                 NaN
## Lag 5e+05                       0.01506449                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0             1.000000000         1.000000000                 NaN
## Lag 1e+05         0.030897518         0.041777794                 NaN
## Lag 2e+05        -0.001292263         0.027269941                 NaN
## Lag 3e+05         0.011655779         0.019210357                 NaN
## Lag 4e+05        -0.007796472        -0.030315018                 NaN
## Lag 5e+05         0.023331975         0.006777293                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0              1.00000000         1.000000000          1.00000000
## Lag 1e+05          0.01967175         0.014199622          0.17320150
## Lag 2e+05          0.01210264        -0.010027096          0.08496247
## Lag 3e+05          0.00436030         0.002427261          0.05083311
## Lag 4e+05          0.00312743         0.011437854          0.04220305
## Lag 5e+05         -0.02187181        -0.002679297          0.03447290
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000           1.000000000          1.000000000
## Lag 1e+05            0.18883003           0.157859893          0.139188321
## Lag 2e+05            0.08374807           0.058160208          0.014042578
## Lag 3e+05            0.07271627           0.045407341         -0.037193306
## Lag 4e+05            0.06176667           0.018787726          0.001957531
## Lag 5e+05            0.01071464          -0.006124526          0.006344720
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000                  NaN           1.00000000
## Lag 1e+05          0.064542272                  NaN           0.18591604
## Lag 2e+05          0.017079930                  NaN           0.11044324
## Lag 3e+05          0.006898274                  NaN           0.08176853
## Lag 4e+05          0.019434732                  NaN           0.03210445
## Lag 5e+05         -0.003484503                  NaN           0.02573763
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0               1.00000000      1.000000000
## Lag 1e+05           0.10559182      0.061357354
## Lag 2e+05           0.02688983      0.035338247
## Lag 3e+05           0.05394412     -0.000495449
## Lag 4e+05           0.02036580      0.011207598
## Lag 5e+05           0.01579862      0.021349941
## Chain 2 
##                 edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.00000000                       1.00000000
## Lag 1e+05  0.09851394                       0.21853318
## Lag 2e+05  0.00465723                       0.09021363
## Lag 3e+05  0.02901316                       0.07309348
## Lag 4e+05  0.02634481                       0.06132681
## Lag 5e+05 -0.01836916                       0.01615536
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0001280749
## Lag 2e+05                    -0.0130842366
## Lag 3e+05                     0.0083251224
## Lag 4e+05                    -0.0234647178
## Lag 5e+05                     0.0109897327
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0022883946
## Lag 2e+05                     0.0045646716
## Lag 3e+05                    -0.0158709546
## Lag 4e+05                    -0.0082576830
## Lag 5e+05                     0.0001882364
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.211011461
## Lag 2e+05                      0.079612254
## Lag 3e+05                      0.043045546
## Lag 4e+05                      0.026893036
## Lag 5e+05                      0.002733771
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.16915425                 NaN
## Lag 2e+05                       0.04932373                 NaN
## Lag 3e+05                       0.02901708                 NaN
## Lag 4e+05                       0.03255304                 NaN
## Lag 5e+05                      -0.01108038                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0             1.000000000        1.0000000000                 NaN
## Lag 1e+05         0.022145616        0.0419144789                 NaN
## Lag 2e+05        -0.006458181       -0.0045295890                 NaN
## Lag 3e+05         0.003662739        0.0075451858                 NaN
## Lag 4e+05         0.006207209        0.0001433991                 NaN
## Lag 5e+05        -0.013992339       -0.0198887617                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000         1.000000000          1.00000000
## Lag 1e+05         0.022569499         0.012458794          0.12652886
## Lag 2e+05        -0.009981778        -0.009569254          0.02481685
## Lag 3e+05        -0.026933152         0.038891371          0.02490411
## Lag 4e+05        -0.007014203        -0.005074752          0.02248224
## Lag 5e+05         0.023220825        -0.001484974         -0.01870880
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000          1.000000000
## Lag 1e+05            0.20033710            0.12405030          0.114127636
## Lag 2e+05            0.08713704            0.05842359          0.018665401
## Lag 3e+05            0.05525040            0.03748294         -0.015773112
## Lag 4e+05            0.03656093            0.03107247         -0.019058025
## Lag 5e+05            0.01537730            0.02206032         -0.008724877
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000                  NaN           1.00000000
## Lag 1e+05          0.029522128                  NaN           0.16400749
## Lag 2e+05          0.008307655                  NaN           0.05451279
## Lag 3e+05          0.028920537                  NaN           0.01066813
## Lag 4e+05          0.005454676                  NaN           0.02906446
## Lag 5e+05         -0.016745217                  NaN           0.01012355
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.095052235      0.030373828
## Lag 2e+05         -0.004965831     -0.016397981
## Lag 3e+05          0.025622786      0.008558745
## Lag 4e+05          0.024584956      0.011814286
## Lag 5e+05         -0.036319362     -0.013394557
## Chain 3 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                     1.0000000000
## Lag 1e+05  0.110850884                     0.2045724509
## Lag 2e+05  0.035746543                     0.0759081402
## Lag 3e+05  0.003551163                     0.0376044322
## Lag 4e+05 -0.019560883                     0.0009708908
## Lag 5e+05  0.018305229                     0.0037092706
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.052868654
## Lag 2e+05                      0.003509665
## Lag 3e+05                      0.003520507
## Lag 4e+05                     -0.012880474
## Lag 5e+05                      0.005844861
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.022456189
## Lag 2e+05                      0.008569368
## Lag 3e+05                      0.008446430
## Lag 4e+05                     -0.030634198
## Lag 5e+05                      0.021013016
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.236689961
## Lag 2e+05                      0.071673068
## Lag 3e+05                      0.044605931
## Lag 4e+05                      0.029816792
## Lag 5e+05                      0.009577031
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                          1.000000000                 NaN
## Lag 1e+05                      0.175382717                 NaN
## Lag 2e+05                      0.092716784                 NaN
## Lag 3e+05                      0.047509727                 NaN
## Lag 4e+05                     -0.005429058                 NaN
## Lag 5e+05                     -0.018316763                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0             1.000000000          1.00000000                 NaN
## Lag 1e+05         0.034360591          0.02779117                 NaN
## Lag 2e+05         0.003654276          0.01453083                 NaN
## Lag 3e+05        -0.019856003         -0.01070607                 NaN
## Lag 4e+05         0.007431766         -0.02703800                 NaN
## Lag 5e+05        -0.003801274          0.03091276                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0            1.0000000000         1.000000000         1.000000000
## Lag 1e+05        0.0121716735        -0.008946987         0.127859282
## Lag 2e+05        0.0306529970        -0.000548480         0.023346211
## Lag 3e+05       -0.0001698867         0.010112117         0.003778167
## Lag 4e+05        0.0226946378        -0.013097869         0.003143204
## Lag 5e+05       -0.0126464931        -0.002681296         0.010938883
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.19360889            0.17742074           0.11861781
## Lag 2e+05            0.05501734            0.06896667           0.03432955
## Lag 3e+05            0.04700043            0.01051782          -0.03522659
## Lag 4e+05            0.02781616           -0.01116838           0.01106236
## Lag 5e+05            0.01646321           -0.01233376           0.01392155
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0             1.0000000000                  NaN           1.00000000
## Lag 1e+05         0.0534262624                  NaN           0.19026381
## Lag 2e+05        -0.0002709374                  NaN           0.09187678
## Lag 3e+05         0.0038533056                  NaN           0.05791957
## Lag 4e+05        -0.0376999340                  NaN           0.02293692
## Lag 5e+05        -0.0072821303                  NaN           0.03594087
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.090105325      0.034708958
## Lag 2e+05          0.026797289      0.006300416
## Lag 3e+05          0.009574458      0.008682453
## Lag 4e+05         -0.006867847     -0.043148882
## Lag 5e+05          0.016775531      0.023547176
## Chain 4 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                       1.00000000
## Lag 1e+05 0.09290302                       0.22096252
## Lag 2e+05 0.08370338                       0.14469869
## Lag 3e+05 0.01409275                       0.05747672
## Lag 4e+05 0.01045947                       0.01430869
## Lag 5e+05 0.01223927                       0.03403705
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.022036970
## Lag 2e+05                      0.001034541
## Lag 3e+05                     -0.023540139
## Lag 4e+05                     -0.001068858
## Lag 5e+05                      0.003695188
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0135013331
## Lag 2e+05                    -0.0038894548
## Lag 3e+05                     0.0299238234
## Lag 4e+05                    -0.0057522120
## Lag 5e+05                    -0.0007255634
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.22692099
## Lag 2e+05                       0.12767433
## Lag 3e+05                       0.05053357
## Lag 4e+05                       0.03511662
## Lag 5e+05                       0.02597186
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.21370478                 NaN
## Lag 2e+05                       0.09377703                 NaN
## Lag 3e+05                       0.06226017                 NaN
## Lag 4e+05                       0.04096922                 NaN
## Lag 5e+05                       0.02780972                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0            1.000000e+00          1.00000000                 NaN
## Lag 1e+05       -2.296505e-02          0.04452961                 NaN
## Lag 2e+05        8.674597e-06          0.02950926                 NaN
## Lag 3e+05       -1.426885e-02          0.01237768                 NaN
## Lag 4e+05        5.343195e-03          0.00754383                 NaN
## Lag 5e+05       -9.326690e-03          0.00455378                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000         1.000000000          1.00000000
## Lag 1e+05         0.007417531         0.006562911          0.13795876
## Lag 2e+05         0.003322203        -0.016154668          0.08754619
## Lag 3e+05         0.014340663        -0.036365082          0.01336508
## Lag 4e+05        -0.005843071        -0.007413842          0.01985218
## Lag 5e+05        -0.006555389         0.003993605          0.02127420
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.16277034            0.12330704           0.09469469
## Lag 2e+05            0.09199799            0.05124610           0.04193576
## Lag 3e+05            0.02830096            0.01620112           0.01188139
## Lag 4e+05            0.02024980            0.02805925          -0.01004093
## Lag 5e+05            0.01575226            0.01114279          -0.01454849
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0             1.0000000000                  NaN           1.00000000
## Lag 1e+05         0.0419516743                  NaN           0.18552813
## Lag 2e+05         0.0407468114                  NaN           0.10444687
## Lag 3e+05         0.0154689215                  NaN           0.03140037
## Lag 4e+05         0.0108476879                  NaN           0.03183470
## Lag 5e+05        -0.0004602954                  NaN           0.01417561
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.108550643      0.039794851
## Lag 2e+05          0.059205557      0.033736619
## Lag 3e+05          0.032402334      0.007711898
## Lag 4e+05         -0.002042805      0.011647397
## Lag 5e+05          0.011185263      0.002771941
## Chain 5 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.130721798                      0.233413356
## Lag 2e+05  0.017395593                      0.098349749
## Lag 3e+05 -0.003285385                      0.049964824
## Lag 4e+05  0.006445234                      0.020593925
## Lag 5e+05  0.013881532                      0.009026563
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.034494072
## Lag 2e+05                     -0.011047010
## Lag 3e+05                     -0.002904326
## Lag 4e+05                      0.022721800
## Lag 5e+05                      0.010460012
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.008620757
## Lag 2e+05                      0.005918871
## Lag 3e+05                     -0.020454197
## Lag 4e+05                      0.024448376
## Lag 5e+05                     -0.011980332
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.24977292
## Lag 2e+05                       0.09874657
## Lag 3e+05                       0.04642508
## Lag 4e+05                       0.04689410
## Lag 5e+05                       0.03041982
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                          1.000000000                 NaN
## Lag 1e+05                      0.197186335                 NaN
## Lag 2e+05                      0.045925713                 NaN
## Lag 3e+05                     -0.002232392                 NaN
## Lag 4e+05                      0.012653506                 NaN
## Lag 5e+05                      0.037688541                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0             1.000000000        1.0000000000                 NaN
## Lag 1e+05        -0.036218711        0.0502643096                 NaN
## Lag 2e+05        -0.014938569       -0.0077994297                 NaN
## Lag 3e+05        -0.016813726       -0.0112128636                 NaN
## Lag 4e+05         0.004786666        0.0002880085                 NaN
## Lag 5e+05         0.026454991       -0.0072216605                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0            1.0000000000         1.000000000         1.000000000
## Lag 1e+05        0.0081167992        -0.004542726         0.163637038
## Lag 2e+05       -0.0181545950        -0.021500628         0.033449098
## Lag 3e+05       -0.0005634124        -0.006186686         0.006117211
## Lag 4e+05        0.0147676630         0.009414542         0.007547344
## Lag 5e+05        0.0108830199         0.008333332         0.012164163
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000           1.000000000          1.000000000
## Lag 1e+05           0.202076327           0.151422830          0.067799672
## Lag 2e+05           0.088058210           0.062093049          0.026084887
## Lag 3e+05           0.054610626          -0.002354675          0.001894431
## Lag 4e+05           0.045577992          -0.005169030         -0.031561689
## Lag 5e+05           0.007259537           0.006352411          0.005808390
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000                  NaN          1.000000000
## Lag 1e+05          0.063983032                  NaN          0.187134725
## Lag 2e+05          0.020842226                  NaN          0.065161811
## Lag 3e+05          0.009544928                  NaN          0.031838462
## Lag 4e+05          0.018137053                  NaN          0.004461249
## Lag 5e+05         -0.005467869                  NaN         -0.001010826
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0               1.00000000     1.0000000000
## Lag 1e+05           0.10849902     0.0523994498
## Lag 2e+05           0.03333136     0.0009634913
## Lag 3e+05           0.01856035    -0.0029688872
## Lag 4e+05           0.01044798    -0.0107429037
## Lag 5e+05           0.00989780    -0.0044432619
## Chain 6 
##                 edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.00000000                       1.00000000
## Lag 1e+05  0.14069240                       0.25410382
## Lag 2e+05  0.05753907                       0.08951555
## Lag 3e+05  0.01409801                       0.04707362
## Lag 4e+05 -0.01511485                       0.01836147
## Lag 5e+05 -0.03317312                       0.01361313
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.018853476
## Lag 2e+05                     -0.012706376
## Lag 3e+05                     -0.019656284
## Lag 4e+05                     -0.001300013
## Lag 5e+05                     -0.013015789
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.002478975
## Lag 2e+05                      0.025290883
## Lag 3e+05                      0.022907461
## Lag 4e+05                     -0.020268594
## Lag 5e+05                     -0.024916692
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.24548359
## Lag 2e+05                       0.11052166
## Lag 3e+05                       0.05432373
## Lag 4e+05                       0.01805815
## Lag 5e+05                       0.01153243
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.20488917                 NaN
## Lag 2e+05                       0.07941400                 NaN
## Lag 3e+05                       0.03173223                 NaN
## Lag 4e+05                       0.01207945                 NaN
## Lag 5e+05                       0.00937207                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0             1.000000000          1.00000000                 NaN
## Lag 1e+05        -0.027185814          0.06690508                 NaN
## Lag 2e+05         0.003288122          0.01175922                 NaN
## Lag 3e+05        -0.009807208         -0.02774368                 NaN
## Lag 4e+05         0.028502131         -0.02004848                 NaN
## Lag 5e+05         0.004224853         -0.03413976                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0              1.00000000         1.000000000         1.000000000
## Lag 1e+05          0.01838116        -0.005016124         0.187090969
## Lag 2e+05         -0.01803243         0.006771741         0.067961712
## Lag 3e+05         -0.02990333         0.014055081         0.026932243
## Lag 4e+05         -0.01673471        -0.038591377        -0.006263361
## Lag 5e+05         -0.01010815         0.023505453        -0.020468104
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000           1.000000000           1.00000000
## Lag 1e+05           0.180672090           0.142503791           0.08761510
## Lag 2e+05           0.075524754           0.058265141           0.01805396
## Lag 3e+05           0.061847868           0.024090601           0.00906693
## Lag 4e+05           0.026977571           0.009134784          -0.01343445
## Lag 5e+05           0.009238511           0.011288356          -0.04688926
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0             1.000000e+00                  NaN           1.00000000
## Lag 1e+05         7.567260e-02                  NaN           0.19157351
## Lag 2e+05         9.798232e-03                  NaN           0.09035011
## Lag 3e+05        -9.815243e-05                  NaN           0.09173392
## Lag 4e+05         1.463847e-03                  NaN           0.01895014
## Lag 5e+05        -8.933926e-03                  NaN           0.02055896
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0             1.0000000000      1.000000000
## Lag 1e+05         0.1092925302      0.064690009
## Lag 2e+05         0.0620218376      0.002791682
## Lag 3e+05         0.0195991952     -0.003413749
## Lag 4e+05         0.0001621148     -0.022908414
## Lag 5e+05        -0.0219618895     -0.059959659
## Chain 7 
##                 edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.000000000                       1.00000000
## Lag 1e+05 0.106442451                       0.22304679
## Lag 2e+05 0.054545171                       0.11085315
## Lag 3e+05 0.010244780                       0.05390366
## Lag 4e+05 0.008379283                       0.04786309
## Lag 5e+05 0.001436509                       0.01574710
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0187607101
## Lag 2e+05                     0.0002399737
## Lag 3e+05                     0.0073210093
## Lag 4e+05                    -0.0148097631
## Lag 5e+05                     0.0059137263
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0081020194
## Lag 2e+05                     0.0008276591
## Lag 3e+05                     0.0160971527
## Lag 4e+05                    -0.0138968583
## Lag 5e+05                    -0.0334847104
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.19619313
## Lag 2e+05                       0.10398124
## Lag 3e+05                       0.05240826
## Lag 4e+05                       0.01139080
## Lag 5e+05                       0.01371625
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                          1.000000000                 NaN
## Lag 1e+05                      0.169365915                 NaN
## Lag 2e+05                      0.068535796                 NaN
## Lag 3e+05                      0.026832880                 NaN
## Lag 4e+05                      0.013426846                 NaN
## Lag 5e+05                     -0.005095864                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0            1.0000000000         1.000000000                 NaN
## Lag 1e+05       -0.0274154382         0.031315447                 NaN
## Lag 2e+05        0.0081350637         0.002392960                 NaN
## Lag 3e+05        0.0112026821        -0.016599074                 NaN
## Lag 4e+05       -0.0009366492        -0.003546458                 NaN
## Lag 5e+05        0.0110438988        -0.013107637                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000        1.0000000000         1.000000000
## Lag 1e+05         0.016095865       -0.0009489891         0.139081885
## Lag 2e+05        -0.019184557        0.0057107212         0.076957529
## Lag 3e+05         0.006306646       -0.0064336694         0.018377866
## Lag 4e+05         0.009973494       -0.0371980918         0.021976716
## Lag 5e+05        -0.005375546       -0.0022381215         0.003570699
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000           1.000000000         1.0000000000
## Lag 1e+05           0.167837104           0.127159838         0.0905548928
## Lag 2e+05           0.061230989           0.062415893         0.0478200022
## Lag 3e+05           0.044648689           0.027788126         0.0021618897
## Lag 4e+05           0.022859761           0.013991864         0.0170659031
## Lag 5e+05           0.005116968           0.002564168        -0.0008612144
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000                  NaN           1.00000000
## Lag 1e+05           0.07713654                  NaN           0.18716084
## Lag 2e+05           0.04809892                  NaN           0.08682495
## Lag 3e+05          -0.01414061                  NaN           0.03460278
## Lag 4e+05           0.02001543                  NaN           0.01142406
## Lag 5e+05           0.01264556                  NaN           0.01317376
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000       1.00000000
## Lag 1e+05          0.082205152       0.04032323
## Lag 2e+05          0.016563075       0.03330750
## Lag 3e+05         -0.006783307       0.01390140
## Lag 4e+05         -0.011532176      -0.01811727
## Lag 5e+05         -0.012201468       0.01440736
## Chain 8 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                      1.000000000
## Lag 1e+05 0.12800688                      0.228846359
## Lag 2e+05 0.03640586                      0.074019641
## Lag 3e+05 0.02193200                      0.021605906
## Lag 4e+05 0.01781764                      0.002147398
## Lag 5e+05 0.03012589                      0.006600887
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.014249127
## Lag 2e+05                      0.002788277
## Lag 3e+05                     -0.006109748
## Lag 4e+05                      0.013792129
## Lag 5e+05                      0.003097483
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0064136409
## Lag 2e+05                    -0.0156403287
## Lag 3e+05                     0.0140782482
## Lag 4e+05                     0.0081420585
## Lag 5e+05                    -0.0001497984
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.21925900
## Lag 2e+05                       0.10639515
## Lag 3e+05                       0.05767041
## Lag 4e+05                       0.03890008
## Lag 5e+05                       0.03525085
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                         1.0000000000                 NaN
## Lag 1e+05                     0.1760974068                 NaN
## Lag 2e+05                     0.0960475433                 NaN
## Lag 3e+05                    -0.0007477265                 NaN
## Lag 4e+05                     0.0142965277                 NaN
## Lag 5e+05                     0.0105918108                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0             1.000000000         1.000000000                 NaN
## Lag 1e+05        -0.007109414         0.072035015                 NaN
## Lag 2e+05        -0.014039238         0.011703915                 NaN
## Lag 3e+05         0.005964218         0.004433604                 NaN
## Lag 4e+05         0.003000583        -0.013906887                 NaN
## Lag 5e+05        -0.002389498         0.027072088                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000        1.0000000000          1.00000000
## Lag 1e+05        -0.001180558       -0.0177870546          0.15617883
## Lag 2e+05         0.004157036       -0.0237756911          0.04660473
## Lag 3e+05        -0.015685389       -0.0114954329          0.01819985
## Lag 4e+05        -0.002899550       -0.0159948860          0.01769235
## Lag 5e+05         0.019110711       -0.0005673584          0.02851493
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000          1.000000000
## Lag 1e+05            0.21077731            0.13696625          0.093803187
## Lag 2e+05            0.10647140            0.04576534          0.040668977
## Lag 3e+05            0.04155740            0.02883993          0.009567086
## Lag 4e+05            0.03856546            0.01345383         -0.011941063
## Lag 5e+05            0.02688688            0.01106580         -0.006763714
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000                  NaN           1.00000000
## Lag 1e+05          0.073031460                  NaN           0.17761114
## Lag 2e+05          0.023751034                  NaN           0.03887062
## Lag 3e+05          0.008722027                  NaN          -0.00461939
## Lag 4e+05          0.015131722                  NaN          -0.02454995
## Lag 5e+05          0.030364306                  NaN          -0.03245683
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0               1.00000000      1.000000000
## Lag 1e+05           0.12467017      0.073039137
## Lag 2e+05           0.04352901      0.001162365
## Lag 3e+05           0.03079318      0.008890373
## Lag 4e+05           0.01128837     -0.002510417
## Lag 5e+05           0.02350377      0.014697148
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           0.8207                           0.5785 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          -0.4688                          -0.8373 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           1.9742                          -1.3980 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                          -2.2341 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                           1.2678                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                           0.1512                           1.6010 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                           0.3191                           1.3428 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          -0.5170                           1.9403 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                           0.9870                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                           1.1859                           0.8032 
##                 absdiff.sqrt.age 
##                           0.7218 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.41179574                       0.56290220 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.63920686                       0.40242237 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.04836395                       0.16209996 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                       0.02547739 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                       0.20485390                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                       0.87982287                       0.10937466 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                       0.74966366                       0.17933074 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.60513651                       0.05233799 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.32364839                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.23565946                       0.42188162 
##                 absdiff.sqrt.age 
##                       0.47042355 
## Joint P-value (lower = worse):  7.192737e-05 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.74678                          0.36201 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.85052                          1.49265 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.47319                          0.05294 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                         -0.47253 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                         -0.68590                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                          0.25552                         -0.11910 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                          1.01584                          1.95508 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          0.17840                          0.54138 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                          0.43254                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          0.43391                         -0.22207 
##                 absdiff.sqrt.age 
##                         -0.12128 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.45519391                       0.71734374 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.39503835                       0.13552806 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.63607830                       0.95777936 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                       0.63654511 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                       0.49277925                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                       0.79832084                       0.90519589 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                       0.30970359                       0.05057391 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.85841050                       0.58824778 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.66535065                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.66435265                       0.82425563 
##                 absdiff.sqrt.age 
##                       0.90346898 
## Joint P-value (lower = worse):  0.9976415 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.24627                         -0.63796 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.86356                          1.38580 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.22927                          0.22737 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                          0.05179 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                         -0.97246                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                          0.48807                          0.02398 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                          0.49475                          1.97949 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                         -0.92833                         -0.29298 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                         -0.66338                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                         -1.44164                         -0.41006 
##                 absdiff.sqrt.age 
##                         -0.09754 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.80547220                       0.52350213 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.38783106                       0.16580682 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.81866266                       0.82013290 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                       0.95869964 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                       0.33082298                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                       0.62550290                       0.98086691 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                       0.62077971                       0.04776088 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.35323447                       0.76953881 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.50708679                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.14940282                       0.68176070 
##                 absdiff.sqrt.age 
##                       0.92229876 
## Joint P-value (lower = worse):  0.627 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           0.7721                           1.1876 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          -2.9923                           0.6276 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           1.0271                           2.7477 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                           1.1578 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                          -0.1084                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                          -0.1001                          -0.3093 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                           0.9169                          -0.9699 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                           1.0964                           0.6150 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                           1.4477                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                           0.8602                           1.2264 
##                 absdiff.sqrt.age 
##                           1.9215 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                      0.440047066                      0.234988533 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                      0.002768609                      0.530288765 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                      0.304385475                      0.006002069 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                      0.246953574 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                      0.913672067                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                      0.920289006                      0.757122745 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                      0.359170227                      0.332079431 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                      0.272915254                      0.538537377 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                      0.147693816                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                      0.389673757                      0.220035332 
##                 absdiff.sqrt.age 
##                      0.054663907 
## Joint P-value (lower = worse):  0.07127315 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -1.53730                         -1.48822 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -2.56315                         -0.42909 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -2.18686                         -0.16159 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                         -0.94550 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                         -0.01468                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                          0.14552                         -0.96560 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                         -1.51938                         -0.41085 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                         -0.02716                         -0.82823 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                         -1.14127                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          0.46499                         -1.74729 
##                 absdiff.sqrt.age 
##                         -1.76675 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.12421964                       0.13669196 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.01037269                       0.66785625 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.02875283                       0.87162722 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                       0.34440116 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                       0.98828433                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                       0.88430075                       0.33424283 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                       0.12866654                       0.68118208 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.97833492                       0.40754036 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.25375942                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.64193745                       0.08058780 
##                 absdiff.sqrt.age 
##                       0.07726946 
## Joint P-value (lower = worse):  0.9898051 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           1.0988                           0.6876 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           1.4478                           0.5301 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           0.6821                           0.7426 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                          -0.5208 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                           0.5956                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                          -0.1728                          -0.2371 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                           1.0053                          -0.0778 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                           0.3872                          -0.3609 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                          -0.5479                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          -0.3047                           0.9401 
##                 absdiff.sqrt.age 
##                           1.3498 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.2718768                        0.4916787 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.1476708                        0.5960120 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.4951679                        0.4577357 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                        0.6025236 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                        0.5514438                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                        0.8627787                        0.8125556 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                        0.3147541                        0.9379835 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                        0.6986437                        0.7181730 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                        0.5837542                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                        0.7606077                        0.3471598 
##                 absdiff.sqrt.age 
##                        0.1770947 
## Joint P-value (lower = worse):  0.9915364 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.07970                          0.50962 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.20269                         -0.18958 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -1.14221                         -0.60206 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                         -0.83506 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                         -1.02690                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                         -1.87010                         -1.29421 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                          0.74737                         -0.66249 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          0.47761                         -0.02569 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                          0.17669                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                         -0.08925                          0.07375 
##                 absdiff.sqrt.age 
##                          0.15811 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.93647731                       0.61031565 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.83938008                       0.84963758 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.25336583                       0.54713119 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                       0.40368578 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                       0.30446622                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                       0.06147027                       0.19559156 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                       0.45484323                       0.50765862 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.63292581                       0.97950527 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.85974989                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.92888619                       0.94121187 
##                 absdiff.sqrt.age 
##                       0.87436988 
## Joint P-value (lower = worse):  1.026273e-06 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -0.86872                          0.05296 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.66440                          0.47943 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.86342                         -1.03401 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                         -0.60238 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                          0.85162                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                          0.71588                         -0.14962 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                         -1.21514                         -0.34448 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                         -1.15976                          0.35454 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                         -0.71856                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                         -1.18588                         -0.33867 
##                 absdiff.sqrt.age 
##                         -0.83806 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.3850001                        0.9577676 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.5064367                        0.6316346 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.3879090                        0.3011319 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                        0.5469236 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                        0.3944235                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                        0.4740640                        0.8810611 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                        0.2243131                        0.7304878 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                        0.2461447                        0.7229339 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                        0.4724136                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                        0.2356682                        0.7348579 
##                 absdiff.sqrt.age 
##                        0.4019945 
## Joint P-value (lower = worse):  0.980291 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 8

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                                      Mean     SD Naive SE Time-series SE
## edges                            -3.56556 21.732  0.12547        0.16451
## nodefactor.deg.main.deg.pers.0.1  0.58459 14.317  0.08266        0.13121
## nodefactor.deg.main.deg.pers.0.2 -0.02164  6.206  0.03583        0.03757
## nodefactor.deg.main.deg.pers.1.0 -0.13124  6.164  0.03559        0.03555
## nodefactor.deg.main.deg.pers.1.1 -0.19955 12.490  0.07211        0.10952
## nodefactor.deg.main.deg.pers.1.2  0.16013 12.950  0.07477        0.10936
## nodefactor.riskg.O2              -0.40092  0.000  0.00000        0.00000
## nodefactor.riskg.O3              -1.42575  2.358  0.01361        0.01340
## nodefactor.riskg.O4              -1.52093 11.560  0.06674        0.07646
## nodefactor.riskg.Y1              -1.34908  0.000  0.00000        0.00000
## nodefactor.riskg.Y2              -1.44488  2.612  0.01508        0.01514
## nodefactor.riskg.Y3              -1.48337  8.642  0.04989        0.04920
## nodefactor.riskg.Y4              -1.60022 36.939  0.21327        0.29480
## nodefactor.race..wa.B             6.38990  8.936  0.05159        0.07316
## nodefactor.race..wa.H             6.43339 10.927  0.06309        0.08694
## nodefactor.region.EW              0.05881 11.240  0.06490        0.11338
## nodefactor.region.OW             -0.28319 20.318  0.11730        0.15196
## nodematch.race..wa.B             -2.53754  0.000  0.00000        0.00000
## nodematch.race..wa.H             -6.31580  2.637  0.01523        0.02307
## nodematch.race..wa.O              6.17662 17.091  0.09868        0.12570
## nodematch.region                  0.10257 19.614  0.11324        0.15595
## absdiff.sqrt.age                 -0.13362 22.498  0.12989        0.14851
## 
## 2. Quantiles for each variable:
## 
##                                      2.5%      25%     50%     75%   97.5%
## edges                            -45.5122 -18.5122 -3.5122 10.4878 39.4878
## nodefactor.deg.main.deg.pers.0.1 -27.1754  -9.1754  0.8246  9.8246 29.8246
## nodefactor.deg.main.deg.pers.0.2 -11.2169  -4.2169 -0.2169  3.7831 12.7831
## nodefactor.deg.main.deg.pers.1.0 -11.5677  -4.5677 -0.5677  3.4323 12.4323
## nodefactor.deg.main.deg.pers.1.1 -24.3643  -8.3643 -0.3643  8.6357 24.6357
## nodefactor.deg.main.deg.pers.1.2 -24.8703  -8.8703  0.1297  9.1297 26.1297
## nodefactor.riskg.O2               -0.4009  -0.4009 -0.4009 -0.4009 -0.4009
## nodefactor.riskg.O3               -5.8558  -2.8558 -1.8558  0.1442  4.1442
## nodefactor.riskg.O4              -23.5127  -9.5127 -1.5127  6.4873 21.4873
## nodefactor.riskg.Y1               -1.3491  -1.3491 -1.3491 -1.3491 -1.3491
## nodefactor.riskg.Y2               -6.2024  -3.2024 -1.2024 -0.2024  3.7976
## nodefactor.riskg.Y3              -17.7860  -7.7860 -1.7860  4.2140 16.2140
## nodefactor.riskg.Y4              -73.0115 -27.0115 -2.0115 22.9885 71.9885
## nodefactor.race..wa.B            -10.1865  -0.1865  5.8135 11.8135 23.8135
## nodefactor.race..wa.H            -14.8353  -0.8353  6.1647 14.1647 28.1647
## nodefactor.region.EW             -21.3887  -7.3887 -0.3887  7.6113 22.6113
## nodefactor.region.OW             -39.1591 -14.1591 -0.1591 12.8409 40.8409
## nodematch.race..wa.B              -2.5375  -2.5375 -2.5375 -2.5375 -2.5375
## nodematch.race..wa.H             -11.2750  -8.2750 -6.2750 -4.2750 -0.2750
## nodematch.race..wa.O             -26.8841  -5.8841  6.1159 17.1159 40.1159
## nodematch.region                 -37.8097 -12.8097  0.1903 13.1903 39.1903
## absdiff.sqrt.age                 -43.2664 -15.6723 -0.3485 14.9446 44.8308
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                                      edges
## edges                            1.0000000
## nodefactor.deg.main.deg.pers.0.1 0.5649033
## nodefactor.deg.main.deg.pers.0.2 0.2694434
## nodefactor.deg.main.deg.pers.1.0 0.2715611
## nodefactor.deg.main.deg.pers.1.1 0.5000955
## nodefactor.deg.main.deg.pers.1.2 0.5142969
## nodefactor.riskg.O2                     NA
## nodefactor.riskg.O3              0.1158591
## nodefactor.riskg.O4              0.4352814
## nodefactor.riskg.Y1                     NA
## nodefactor.riskg.Y2              0.1291005
## nodefactor.riskg.Y3              0.3664791
## nodefactor.riskg.Y4              0.9381541
## nodefactor.race..wa.B            0.4130760
## nodefactor.race..wa.H            0.4468128
## nodefactor.region.EW             0.3394088
## nodefactor.region.OW             0.5401817
## nodematch.race..wa.B                    NA
## nodematch.race..wa.H             0.1122183
## nodematch.race..wa.O             0.7871927
## nodematch.region                 0.8972409
## absdiff.sqrt.age                 0.7768734
##                                  nodefactor.deg.main.deg.pers.0.1
## edges                                                  0.56490326
## nodefactor.deg.main.deg.pers.0.1                       1.00000000
## nodefactor.deg.main.deg.pers.0.2                       0.08333554
## nodefactor.deg.main.deg.pers.1.0                       0.08207671
## nodefactor.deg.main.deg.pers.1.1                       0.14906535
## nodefactor.deg.main.deg.pers.1.2                       0.14507264
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.05955529
## nodefactor.riskg.O4                                    0.25816084
## nodefactor.riskg.Y1                                            NA
## nodefactor.riskg.Y2                                    0.07741975
## nodefactor.riskg.Y3                                    0.19732586
## nodefactor.riskg.Y4                                    0.52845477
## nodefactor.race..wa.B                                  0.23428473
## nodefactor.race..wa.H                                  0.20322226
## nodefactor.region.EW                                   0.18021199
## nodefactor.region.OW                                   0.28328868
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.03848297
## nodematch.race..wa.O                                   0.47180050
## nodematch.region                                       0.50828937
## absdiff.sqrt.age                                       0.44651696
##                                  nodefactor.deg.main.deg.pers.0.2
## edges                                                  0.26944339
## nodefactor.deg.main.deg.pers.0.1                       0.08333554
## nodefactor.deg.main.deg.pers.0.2                       1.00000000
## nodefactor.deg.main.deg.pers.1.0                       0.03603181
## nodefactor.deg.main.deg.pers.1.1                       0.06563728
## nodefactor.deg.main.deg.pers.1.2                       0.06562443
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.03407851
## nodefactor.riskg.O4                                    0.10132169
## nodefactor.riskg.Y1                                            NA
## nodefactor.riskg.Y2                                    0.03882566
## nodefactor.riskg.Y3                                    0.10232870
## nodefactor.riskg.Y4                                    0.25646787
## nodefactor.race..wa.B                                  0.12133873
## nodefactor.race..wa.H                                  0.11507760
## nodefactor.region.EW                                   0.07865904
## nodefactor.region.OW                                   0.16534867
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.03166144
## nodematch.race..wa.O                                   0.21047234
## nodematch.region                                       0.24611996
## absdiff.sqrt.age                                       0.21474570
##                                  nodefactor.deg.main.deg.pers.1.0
## edges                                                  0.27156113
## nodefactor.deg.main.deg.pers.0.1                       0.08207671
## nodefactor.deg.main.deg.pers.0.2                       0.03603181
## nodefactor.deg.main.deg.pers.1.0                       1.00000000
## nodefactor.deg.main.deg.pers.1.1                       0.06518100
## nodefactor.deg.main.deg.pers.1.2                       0.06873217
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.03918591
## nodefactor.riskg.O4                                    0.10932522
## nodefactor.riskg.Y1                                            NA
## nodefactor.riskg.Y2                                    0.04048822
## nodefactor.riskg.Y3                                    0.10015884
## nodefactor.riskg.Y4                                    0.25651913
## nodefactor.race..wa.B                                  0.08633422
## nodefactor.race..wa.H                                  0.13702669
## nodefactor.region.EW                                   0.11046576
## nodefactor.region.OW                                   0.13101604
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.04850722
## nodematch.race..wa.O                                   0.22003338
## nodematch.region                                       0.24509129
## absdiff.sqrt.age                                       0.21756883
##                                  nodefactor.deg.main.deg.pers.1.1
## edges                                                  0.50009552
## nodefactor.deg.main.deg.pers.0.1                       0.14906535
## nodefactor.deg.main.deg.pers.0.2                       0.06563728
## nodefactor.deg.main.deg.pers.1.0                       0.06518100
## nodefactor.deg.main.deg.pers.1.1                       1.00000000
## nodefactor.deg.main.deg.pers.1.2                       0.13266756
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.06786679
## nodefactor.riskg.O4                                    0.23778196
## nodefactor.riskg.Y1                                            NA
## nodefactor.riskg.Y2                                    0.07421409
## nodefactor.riskg.Y3                                    0.19548549
## nodefactor.riskg.Y4                                    0.45870393
## nodefactor.race..wa.B                                  0.17006609
## nodefactor.race..wa.H                                  0.22836944
## nodefactor.region.EW                                   0.10401094
## nodefactor.region.OW                                   0.21882479
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.05367842
## nodematch.race..wa.O                                   0.40923899
## nodematch.region                                       0.46297568
## absdiff.sqrt.age                                       0.39276388
##                                  nodefactor.deg.main.deg.pers.1.2
## edges                                                  0.51429688
## nodefactor.deg.main.deg.pers.0.1                       0.14507264
## nodefactor.deg.main.deg.pers.0.2                       0.06562443
## nodefactor.deg.main.deg.pers.1.0                       0.06873217
## nodefactor.deg.main.deg.pers.1.1                       0.13266756
## nodefactor.deg.main.deg.pers.1.2                       1.00000000
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.06476156
## nodefactor.riskg.O4                                    0.22262140
## nodefactor.riskg.Y1                                            NA
## nodefactor.riskg.Y2                                    0.05998563
## nodefactor.riskg.Y3                                    0.17472635
## nodefactor.riskg.Y4                                    0.48621883
## nodefactor.race..wa.B                                  0.19236877
## nodefactor.race..wa.H                                  0.26595963
## nodefactor.region.EW                                   0.22527172
## nodefactor.region.OW                                   0.28780105
## nodematch.race..wa.B                                           NA
## nodematch.race..wa.H                                   0.08270714
## nodematch.race..wa.O                                   0.39608106
## nodematch.region                                       0.45317539
## absdiff.sqrt.age                                       0.39000963
##                                  nodefactor.riskg.O2 nodefactor.riskg.O3
## edges                                             NA         0.115859142
## nodefactor.deg.main.deg.pers.0.1                  NA         0.059555286
## nodefactor.deg.main.deg.pers.0.2                  NA         0.034078505
## nodefactor.deg.main.deg.pers.1.0                  NA         0.039185912
## nodefactor.deg.main.deg.pers.1.1                  NA         0.067866789
## nodefactor.deg.main.deg.pers.1.2                  NA         0.064761557
## nodefactor.riskg.O2                                1                  NA
## nodefactor.riskg.O3                               NA         1.000000000
## nodefactor.riskg.O4                               NA         0.044150017
## nodefactor.riskg.Y1                               NA                  NA
## nodefactor.riskg.Y2                               NA         0.009015873
## nodefactor.riskg.Y3                               NA         0.025499865
## nodefactor.riskg.Y4                               NA         0.052070392
## nodefactor.race..wa.B                             NA         0.043012059
## nodefactor.race..wa.H                             NA         0.045628899
## nodefactor.region.EW                              NA         0.049651294
## nodefactor.region.OW                              NA         0.059828334
## nodematch.race..wa.B                              NA                  NA
## nodematch.race..wa.H                              NA         0.005075216
## nodematch.race..wa.O                              NA         0.096439096
## nodematch.region                                  NA         0.103399591
## absdiff.sqrt.age                                  NA         0.121198388
##                                  nodefactor.riskg.O4 nodefactor.riskg.Y1
## edges                                     0.43528140                  NA
## nodefactor.deg.main.deg.pers.0.1          0.25816084                  NA
## nodefactor.deg.main.deg.pers.0.2          0.10132169                  NA
## nodefactor.deg.main.deg.pers.1.0          0.10932522                  NA
## nodefactor.deg.main.deg.pers.1.1          0.23778196                  NA
## nodefactor.deg.main.deg.pers.1.2          0.22262140                  NA
## nodefactor.riskg.O2                               NA                  NA
## nodefactor.riskg.O3                       0.04415002                  NA
## nodefactor.riskg.O4                       1.00000000                  NA
## nodefactor.riskg.Y1                               NA                   1
## nodefactor.riskg.Y2                       0.02552813                  NA
## nodefactor.riskg.Y3                       0.07096897                  NA
## nodefactor.riskg.Y4                       0.17800276                  NA
## nodefactor.race..wa.B                     0.16448643                  NA
## nodefactor.race..wa.H                     0.15247803                  NA
## nodefactor.region.EW                      0.14849757                  NA
## nodefactor.region.OW                      0.22633338                  NA
## nodematch.race..wa.B                              NA                  NA
## nodematch.race..wa.H                      0.03223854                  NA
## nodematch.race..wa.O                      0.37495710                  NA
## nodematch.region                          0.39299182                  NA
## absdiff.sqrt.age                          0.43710916                  NA
##                                  nodefactor.riskg.Y2 nodefactor.riskg.Y3
## edges                                    0.129100526          0.36647910
## nodefactor.deg.main.deg.pers.0.1         0.077419751          0.19732586
## nodefactor.deg.main.deg.pers.0.2         0.038825665          0.10232870
## nodefactor.deg.main.deg.pers.1.0         0.040488220          0.10015884
## nodefactor.deg.main.deg.pers.1.1         0.074214092          0.19548549
## nodefactor.deg.main.deg.pers.1.2         0.059985630          0.17472635
## nodefactor.riskg.O2                               NA                  NA
## nodefactor.riskg.O3                      0.009015873          0.02549987
## nodefactor.riskg.O4                      0.025528127          0.07096897
## nodefactor.riskg.Y1                               NA                  NA
## nodefactor.riskg.Y2                      1.000000000          0.02643596
## nodefactor.riskg.Y3                      0.026435958          1.00000000
## nodefactor.riskg.Y4                      0.066446585          0.17155634
## nodefactor.race..wa.B                    0.045227229          0.14008228
## nodefactor.race..wa.H                    0.055428556          0.17486182
## nodefactor.region.EW                     0.042741542          0.13731074
## nodefactor.region.OW                     0.074308806          0.20699243
## nodematch.race..wa.B                              NA                  NA
## nodematch.race..wa.H                     0.014502400          0.04329698
## nodematch.race..wa.O                     0.107306899          0.28762765
## nodematch.region                         0.114717119          0.32645772
## absdiff.sqrt.age                         0.098013314          0.27456098
##                                  nodefactor.riskg.Y4 nodefactor.race..wa.B
## edges                                     0.93815407          4.130760e-01
## nodefactor.deg.main.deg.pers.0.1          0.52845477          2.342847e-01
## nodefactor.deg.main.deg.pers.0.2          0.25646787          1.213387e-01
## nodefactor.deg.main.deg.pers.1.0          0.25651913          8.633422e-02
## nodefactor.deg.main.deg.pers.1.1          0.45870393          1.700661e-01
## nodefactor.deg.main.deg.pers.1.2          0.48621883          1.923688e-01
## nodefactor.riskg.O2                               NA                    NA
## nodefactor.riskg.O3                       0.05207039          4.301206e-02
## nodefactor.riskg.O4                       0.17800276          1.644864e-01
## nodefactor.riskg.Y1                               NA                    NA
## nodefactor.riskg.Y2                       0.06644659          4.522723e-02
## nodefactor.riskg.Y3                       0.17155634          1.400823e-01
## nodefactor.riskg.Y4                       1.00000000          3.958486e-01
## nodefactor.race..wa.B                     0.39584858          1.000000e+00
## nodefactor.race..wa.H                     0.43027726          1.494934e-03
## nodefactor.region.EW                      0.31457332          6.206241e-02
## nodefactor.region.OW                      0.50726758          1.957536e-01
## nodematch.race..wa.B                              NA                    NA
## nodematch.race..wa.H                      0.11047218         -9.592256e-03
## nodematch.race..wa.O                      0.72786391         -5.077277e-05
## nodematch.region                          0.84165381          3.825540e-01
## absdiff.sqrt.age                          0.69840584          3.254149e-01
##                                  nodefactor.race..wa.H
## edges                                      0.446812815
## nodefactor.deg.main.deg.pers.0.1           0.203222259
## nodefactor.deg.main.deg.pers.0.2           0.115077601
## nodefactor.deg.main.deg.pers.1.0           0.137026692
## nodefactor.deg.main.deg.pers.1.1           0.228369439
## nodefactor.deg.main.deg.pers.1.2           0.265959628
## nodefactor.riskg.O2                                 NA
## nodefactor.riskg.O3                        0.045628899
## nodefactor.riskg.O4                        0.152478027
## nodefactor.riskg.Y1                                 NA
## nodefactor.riskg.Y2                        0.055428556
## nodefactor.riskg.Y3                        0.174861822
## nodefactor.riskg.Y4                        0.430277256
## nodefactor.race..wa.B                      0.001494934
## nodefactor.race..wa.H                      1.000000000
## nodefactor.region.EW                       0.281683023
## nodefactor.region.OW                       0.260614720
## nodematch.race..wa.B                                NA
## nodematch.race..wa.H                       0.474413107
## nodematch.race..wa.O                       0.001197067
## nodematch.region                           0.380928125
## absdiff.sqrt.age                           0.339887582
##                                  nodefactor.region.EW nodefactor.region.OW
## edges                                      0.33940879           0.54018165
## nodefactor.deg.main.deg.pers.0.1           0.18021199           0.28328868
## nodefactor.deg.main.deg.pers.0.2           0.07865904           0.16534867
## nodefactor.deg.main.deg.pers.1.0           0.11046576           0.13101604
## nodefactor.deg.main.deg.pers.1.1           0.10401094           0.21882479
## nodefactor.deg.main.deg.pers.1.2           0.22527172           0.28780105
## nodefactor.riskg.O2                                NA                   NA
## nodefactor.riskg.O3                        0.04965129           0.05982833
## nodefactor.riskg.O4                        0.14849757           0.22633338
## nodefactor.riskg.Y1                                NA                   NA
## nodefactor.riskg.Y2                        0.04274154           0.07430881
## nodefactor.riskg.Y3                        0.13731074           0.20699243
## nodefactor.riskg.Y4                        0.31457332           0.50726758
## nodefactor.race..wa.B                      0.06206241           0.19575361
## nodefactor.race..wa.H                      0.28168302           0.26061472
## nodefactor.region.EW                       1.00000000           0.06838438
## nodefactor.region.OW                       0.06838438           1.00000000
## nodematch.race..wa.B                               NA                   NA
## nodematch.race..wa.H                       0.11371495           0.07525120
## nodematch.race..wa.O                       0.23656849           0.42949138
## nodematch.region                           0.19200529           0.43613428
## absdiff.sqrt.age                           0.26240787           0.41692937
##                                  nodematch.race..wa.B nodematch.race..wa.H
## edges                                              NA          0.112218258
## nodefactor.deg.main.deg.pers.0.1                   NA          0.038482973
## nodefactor.deg.main.deg.pers.0.2                   NA          0.031661438
## nodefactor.deg.main.deg.pers.1.0                   NA          0.048507217
## nodefactor.deg.main.deg.pers.1.1                   NA          0.053678420
## nodefactor.deg.main.deg.pers.1.2                   NA          0.082707141
## nodefactor.riskg.O2                                NA                   NA
## nodefactor.riskg.O3                                NA          0.005075216
## nodefactor.riskg.O4                                NA          0.032238540
## nodefactor.riskg.Y1                                NA                   NA
## nodefactor.riskg.Y2                                NA          0.014502400
## nodefactor.riskg.Y3                                NA          0.043296983
## nodefactor.riskg.Y4                                NA          0.110472182
## nodefactor.race..wa.B                              NA         -0.009592256
## nodefactor.race..wa.H                              NA          0.474413107
## nodefactor.region.EW                               NA          0.113714953
## nodefactor.region.OW                               NA          0.075251196
## nodematch.race..wa.B                                1                   NA
## nodematch.race..wa.H                               NA          1.000000000
## nodematch.race..wa.O                               NA         -0.001304986
## nodematch.region                                   NA          0.090817019
## absdiff.sqrt.age                                   NA          0.084850645
##                                  nodematch.race..wa.O nodematch.region
## edges                                    7.871927e-01       0.89724092
## nodefactor.deg.main.deg.pers.0.1         4.718005e-01       0.50828937
## nodefactor.deg.main.deg.pers.0.2         2.104723e-01       0.24611996
## nodefactor.deg.main.deg.pers.1.0         2.200334e-01       0.24509129
## nodefactor.deg.main.deg.pers.1.1         4.092390e-01       0.46297568
## nodefactor.deg.main.deg.pers.1.2         3.960811e-01       0.45317539
## nodefactor.riskg.O2                                NA               NA
## nodefactor.riskg.O3                      9.643910e-02       0.10339959
## nodefactor.riskg.O4                      3.749571e-01       0.39299182
## nodefactor.riskg.Y1                                NA               NA
## nodefactor.riskg.Y2                      1.073069e-01       0.11471712
## nodefactor.riskg.Y3                      2.876277e-01       0.32645772
## nodefactor.riskg.Y4                      7.278639e-01       0.84165381
## nodefactor.race..wa.B                   -5.077277e-05       0.38255396
## nodefactor.race..wa.H                    1.197067e-03       0.38092813
## nodefactor.region.EW                     2.365685e-01       0.19200529
## nodefactor.region.OW                     4.294914e-01       0.43613428
## nodematch.race..wa.B                               NA               NA
## nodematch.race..wa.H                    -1.304986e-03       0.09081702
## nodematch.race..wa.O                     1.000000e+00       0.71131181
## nodematch.region                         7.113118e-01       1.00000000
## absdiff.sqrt.age                         6.134553e-01       0.69671813
##                                  absdiff.sqrt.age
## edges                                  0.77687339
## nodefactor.deg.main.deg.pers.0.1       0.44651696
## nodefactor.deg.main.deg.pers.0.2       0.21474570
## nodefactor.deg.main.deg.pers.1.0       0.21756883
## nodefactor.deg.main.deg.pers.1.1       0.39276388
## nodefactor.deg.main.deg.pers.1.2       0.39000963
## nodefactor.riskg.O2                            NA
## nodefactor.riskg.O3                    0.12119839
## nodefactor.riskg.O4                    0.43710916
## nodefactor.riskg.Y1                            NA
## nodefactor.riskg.Y2                    0.09801331
## nodefactor.riskg.Y3                    0.27456098
## nodefactor.riskg.Y4                    0.69840584
## nodefactor.race..wa.B                  0.32541493
## nodefactor.race..wa.H                  0.33988758
## nodefactor.region.EW                   0.26240787
## nodefactor.region.OW                   0.41692937
## nodematch.race..wa.B                           NA
## nodematch.race..wa.H                   0.08485065
## nodematch.race..wa.O                   0.61345534
## nodematch.region                       0.69671813
## absdiff.sqrt.age                       1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                       1.00000000
## Lag 1e+05  0.199796079                       0.33823053
## Lag 2e+05  0.095443735                       0.17869589
## Lag 3e+05  0.041721041                       0.11585134
## Lag 4e+05  0.010948984                       0.08602792
## Lag 5e+05 -0.002032955                       0.04937575
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.040981629
## Lag 2e+05                      0.007964895
## Lag 3e+05                     -0.001863830
## Lag 4e+05                     -0.009155471
## Lag 5e+05                      0.005612414
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.003443310
## Lag 2e+05                     -0.007021287
## Lag 3e+05                      0.006427541
## Lag 4e+05                     -0.006392591
## Lag 5e+05                     -0.015822543
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.30123266
## Lag 2e+05                       0.19036446
## Lag 3e+05                       0.10649332
## Lag 4e+05                       0.07351287
## Lag 5e+05                       0.06191502
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.27846980                 NaN
## Lag 2e+05                       0.13594240                 NaN
## Lag 3e+05                       0.06738239                 NaN
## Lag 4e+05                       0.03219676                 NaN
## Lag 5e+05                       0.04452777                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0             1.000000000          1.00000000                 NaN
## Lag 1e+05         0.009663466          0.09997852                 NaN
## Lag 2e+05         0.016374387          0.03261030                 NaN
## Lag 3e+05         0.003778940          0.03528523                 NaN
## Lag 4e+05         0.007049766          0.01116145                 NaN
## Lag 5e+05         0.006327473         -0.00914574                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000         1.000000000         1.000000000
## Lag 1e+05         0.021849023         0.009842728         0.239236994
## Lag 2e+05         0.037321516        -0.017829106         0.115849782
## Lag 3e+05        -0.025961507        -0.033475446         0.052777266
## Lag 4e+05         0.008635441        -0.002559349         0.023737626
## Lag 5e+05        -0.036371709        -0.001551359         0.003142448
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000           1.00000000
## Lag 1e+05           0.239332373            0.19100467           0.30309940
## Lag 2e+05           0.104517654            0.12042639           0.20262472
## Lag 3e+05           0.076501952            0.04787429           0.14048014
## Lag 4e+05           0.014299375            0.01311789           0.10352004
## Lag 5e+05           0.006650576            0.03330294           0.08455149
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000                  NaN           1.00000000
## Lag 1e+05          0.170847216                  NaN           0.25743237
## Lag 2e+05          0.070545448                  NaN           0.13019086
## Lag 3e+05          0.043334352                  NaN           0.08650448
## Lag 4e+05          0.007472401                  NaN           0.04920890
## Lag 5e+05          0.022481750                  NaN           0.03777267
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000      1.000000000      1.000000000
## Lag 1e+05           0.18341124      0.249838586      0.105832513
## Lag 2e+05           0.08935911      0.124793929      0.054141220
## Lag 3e+05           0.03650999      0.063592661      0.024150383
## Lag 4e+05           0.01934678      0.020323618      0.009628635
## Lag 5e+05           0.01361759      0.007312366     -0.015354250
## Chain 2 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                       1.00000000
## Lag 1e+05  0.194532678                       0.32612400
## Lag 2e+05  0.079747937                       0.15464800
## Lag 3e+05  0.052295881                       0.09592679
## Lag 4e+05  0.014848555                       0.04416558
## Lag 5e+05 -0.008018408                       0.02052307
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.038011968
## Lag 2e+05                     -0.000219702
## Lag 3e+05                      0.020734355
## Lag 4e+05                     -0.004666774
## Lag 5e+05                      0.012941376
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.015158172
## Lag 2e+05                      0.004655728
## Lag 3e+05                     -0.013043665
## Lag 4e+05                      0.006811322
## Lag 5e+05                      0.016601321
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.29125623
## Lag 2e+05                       0.15961505
## Lag 3e+05                       0.09612892
## Lag 4e+05                       0.03795094
## Lag 5e+05                       0.03317365
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.26785140                 NaN
## Lag 2e+05                       0.15112241                 NaN
## Lag 3e+05                       0.08536794                 NaN
## Lag 4e+05                       0.06648910                 NaN
## Lag 5e+05                       0.02141531                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0              1.00000000         1.000000000                 NaN
## Lag 1e+05         -0.02382610         0.090288470                 NaN
## Lag 2e+05         -0.01616006         0.028970283                 NaN
## Lag 3e+05         -0.01194960         0.026091581                 NaN
## Lag 4e+05          0.01863682         0.000627544                 NaN
## Lag 5e+05          0.01048361        -0.018164753                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000         1.000000000         1.000000000
## Lag 1e+05        -0.004373577        -0.003913656         0.244545580
## Lag 2e+05         0.006687090        -0.047642205         0.116674745
## Lag 3e+05         0.027532041         0.015112247         0.047891811
## Lag 4e+05        -0.004486563         0.027720611         0.017406518
## Lag 5e+05        -0.003934316         0.012496895         0.008775215
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000           1.000000000           1.00000000
## Lag 1e+05            0.21709727           0.211763315           0.30094840
## Lag 2e+05            0.10202676           0.102150474           0.19244994
## Lag 3e+05            0.05679021           0.071424645           0.13784798
## Lag 4e+05            0.02361915           0.007648035           0.07758516
## Lag 5e+05            0.03679189          -0.008274849           0.05840279
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000                  NaN           1.00000000
## Lag 1e+05           0.16305862                  NaN           0.26332223
## Lag 2e+05           0.07467225                  NaN           0.12044739
## Lag 3e+05           0.03938576                  NaN           0.09496718
## Lag 4e+05           0.01357288                  NaN           0.02835765
## Lag 5e+05          -0.02363513                  NaN           0.04477631
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000     1.000000e+00
## Lag 1e+05          0.167273514      0.226235443     1.208087e-01
## Lag 2e+05          0.072944155      0.096175455     1.424128e-02
## Lag 3e+05          0.043473502      0.072548781     3.504512e-05
## Lag 4e+05          0.029809331      0.020371650    -1.572370e-02
## Lag 5e+05         -0.007923479      0.003525331    -1.810176e-02
## Chain 3 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                       1.00000000
## Lag 1e+05 0.19159519                       0.31640603
## Lag 2e+05 0.06817764                       0.15494078
## Lag 3e+05 0.05477417                       0.09494944
## Lag 4e+05 0.03121814                       0.06964662
## Lag 5e+05 0.04837717                       0.04740504
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0210495030
## Lag 2e+05                     0.0007972085
## Lag 3e+05                    -0.0074049461
## Lag 4e+05                     0.0121456934
## Lag 5e+05                    -0.0294604135
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.003893960
## Lag 2e+05                     -0.008973501
## Lag 3e+05                      0.002366056
## Lag 4e+05                     -0.022061648
## Lag 5e+05                     -0.003774530
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.30834774
## Lag 2e+05                       0.14711474
## Lag 3e+05                       0.09110987
## Lag 4e+05                       0.04338031
## Lag 5e+05                       0.02832502
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.23701919                 NaN
## Lag 2e+05                       0.08104542                 NaN
## Lag 3e+05                       0.07885246                 NaN
## Lag 4e+05                       0.04169604                 NaN
## Lag 5e+05                       0.03471014                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0            1.0000000000          1.00000000                 NaN
## Lag 1e+05        0.0055830889          0.10428240                 NaN
## Lag 2e+05        0.0001676994          0.02941532                 NaN
## Lag 3e+05       -0.0069607787          0.01024346                 NaN
## Lag 4e+05       -0.0089027749          0.01987420                 NaN
## Lag 5e+05        0.0161722540          0.03221686                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0            1.0000000000         1.000000000          1.00000000
## Lag 1e+05       -0.0105868429         0.002386419          0.23927333
## Lag 2e+05        0.0005818507        -0.013135539          0.09594067
## Lag 3e+05       -0.0106934998        -0.003354261          0.06506249
## Lag 4e+05       -0.0124726764        -0.021606757          0.04282431
## Lag 5e+05       -0.0042980015         0.009664295          0.05071711
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.22713028            0.20615104           0.28320225
## Lag 2e+05            0.12832070            0.09152105           0.18434255
## Lag 3e+05            0.07215433            0.05836968           0.13367295
## Lag 4e+05            0.07170997            0.02660480           0.09717251
## Lag 5e+05            0.03256345            0.02425171           0.06226597
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000                  NaN           1.00000000
## Lag 1e+05           0.21137095                  NaN           0.24647043
## Lag 2e+05           0.10718583                  NaN           0.11709445
## Lag 3e+05           0.07683349                  NaN           0.06430806
## Lag 4e+05           0.05580938                  NaN           0.03609187
## Lag 5e+05           0.04610206                  NaN          -0.01035316
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000       1.00000000       1.00000000
## Lag 1e+05           0.18050165       0.21766424       0.09955475
## Lag 2e+05           0.06323102       0.09474840       0.02924141
## Lag 3e+05           0.04354017       0.06605972       0.02642329
## Lag 4e+05           0.02114070       0.03506386       0.00946626
## Lag 5e+05           0.01634583       0.04844222       0.02643047
## Chain 4 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                       1.00000000
## Lag 1e+05 0.19508974                       0.30399777
## Lag 2e+05 0.09561936                       0.17172503
## Lag 3e+05 0.04533624                       0.11976034
## Lag 4e+05 0.04045804                       0.07312180
## Lag 5e+05 0.01682589                       0.08161597
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0570494911
## Lag 2e+05                     0.0136460880
## Lag 3e+05                    -0.0049530068
## Lag 4e+05                    -0.0005274976
## Lag 5e+05                    -0.0042729297
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0005910585
## Lag 2e+05                     0.0020307249
## Lag 3e+05                    -0.0461998311
## Lag 4e+05                     0.0092410355
## Lag 5e+05                     0.0055531571
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.33059437
## Lag 2e+05                       0.18153493
## Lag 3e+05                       0.08900810
## Lag 4e+05                       0.04708687
## Lag 5e+05                       0.02967748
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.26611716                 NaN
## Lag 2e+05                       0.12113240                 NaN
## Lag 3e+05                       0.07769468                 NaN
## Lag 4e+05                       0.04917169                 NaN
## Lag 5e+05                       0.03657866                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0            1.000000e+00          1.00000000                 NaN
## Lag 1e+05       -4.908333e-03          0.09725251                 NaN
## Lag 2e+05        7.712042e-05          0.02221627                 NaN
## Lag 3e+05       -1.866551e-02          0.03465193                 NaN
## Lag 4e+05        2.586054e-02          0.02005784                 NaN
## Lag 5e+05       -1.105331e-02          0.00468463                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000        1.0000000000          1.00000000
## Lag 1e+05         0.001100492        0.0107397172          0.24199901
## Lag 2e+05         0.013450788        0.0161079785          0.10763744
## Lag 3e+05        -0.024139047       -0.0044654976          0.04192610
## Lag 4e+05        -0.014011785        0.0009975267          0.03899474
## Lag 5e+05        -0.007906765        0.0095031603          0.02214853
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000           1.00000000
## Lag 1e+05           0.205172331            0.17953038           0.29438739
## Lag 2e+05           0.087367242            0.11243362           0.19859988
## Lag 3e+05           0.066918428            0.09641134           0.14488042
## Lag 4e+05           0.067204376            0.07139344           0.10729392
## Lag 5e+05           0.006020644            0.04083295           0.06815222
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000                  NaN           1.00000000
## Lag 1e+05           0.18754513                  NaN           0.26829534
## Lag 2e+05           0.08460125                  NaN           0.17056742
## Lag 3e+05           0.05032405                  NaN           0.11415735
## Lag 4e+05           0.04636697                  NaN           0.10303574
## Lag 5e+05           0.02065906                  NaN           0.06580521
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000       1.00000000       1.00000000
## Lag 1e+05          0.193779768       0.23541116       0.09977578
## Lag 2e+05          0.100719360       0.10672189       0.04935837
## Lag 3e+05          0.053280728       0.05969507       0.04330065
## Lag 4e+05          0.017945009       0.05741005       0.01727235
## Lag 5e+05         -0.001894853       0.02939976       0.01313848
## Chain 5 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                       1.00000000
## Lag 1e+05 0.18407732                       0.29207536
## Lag 2e+05 0.08092445                       0.15720160
## Lag 3e+05 0.03624481                       0.09584078
## Lag 4e+05 0.01407873                       0.06339874
## Lag 5e+05 0.02544165                       0.03459010
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0526919998
## Lag 2e+05                     0.0262004267
## Lag 3e+05                    -0.0006769669
## Lag 4e+05                    -0.0073265027
## Lag 5e+05                    -0.0169482747
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.012172873
## Lag 2e+05                      0.016775712
## Lag 3e+05                     -0.003324037
## Lag 4e+05                      0.022773895
## Lag 5e+05                      0.040919784
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.31481272
## Lag 2e+05                       0.16520252
## Lag 3e+05                       0.08796297
## Lag 4e+05                       0.06273355
## Lag 5e+05                       0.03719886
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.24812207                 NaN
## Lag 2e+05                       0.11233507                 NaN
## Lag 3e+05                       0.05850476                 NaN
## Lag 4e+05                       0.04305759                 NaN
## Lag 5e+05                       0.05396184                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0             1.000000000         1.000000000                 NaN
## Lag 1e+05        -0.027313676         0.091806599                 NaN
## Lag 2e+05         0.026236161         0.057399394                 NaN
## Lag 3e+05         0.002896822        -0.010161301                 NaN
## Lag 4e+05        -0.002516686         0.005726486                 NaN
## Lag 5e+05         0.022460136        -0.020602137                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000          1.00000000          1.00000000
## Lag 1e+05        -0.025604856         -0.01456240          0.23207456
## Lag 2e+05         0.004541738         -0.01556903          0.10606107
## Lag 3e+05         0.008183799         -0.03740247          0.05332398
## Lag 4e+05         0.015190266          0.01984379          0.02921620
## Lag 5e+05        -0.004910979          0.01043950          0.03901637
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000            1.0000000
## Lag 1e+05            0.23315077            0.23364990            0.2816848
## Lag 2e+05            0.12799418            0.13203565            0.1984260
## Lag 3e+05            0.07153435            0.07126792            0.1649331
## Lag 4e+05            0.01794682            0.01508743            0.1192896
## Lag 5e+05            0.01812468            0.02619630            0.1026080
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000                  NaN           1.00000000
## Lag 1e+05           0.19305664                  NaN           0.20929876
## Lag 2e+05           0.07105775                  NaN           0.12101960
## Lag 3e+05           0.02744183                  NaN           0.06911960
## Lag 4e+05          -0.01262275                  NaN           0.04759661
## Lag 5e+05          -0.01581832                  NaN           0.03074158
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000       1.00000000     1.0000000000
## Lag 1e+05           0.14833761       0.20865936     0.1063360159
## Lag 2e+05           0.05232222       0.08748158     0.0621543237
## Lag 3e+05           0.01236513       0.03525519     0.0155328968
## Lag 4e+05           0.02472844       0.02175787    -0.0132763917
## Lag 5e+05           0.03614763       0.02117444     0.0004385869
## Chain 6 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                       1.00000000
## Lag 1e+05 0.18097969                       0.28425586
## Lag 2e+05 0.08677275                       0.14803187
## Lag 3e+05 0.07974076                       0.10578425
## Lag 4e+05 0.05450908                       0.06876284
## Lag 5e+05 0.03978584                       0.04179915
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.047544204
## Lag 2e+05                      0.014920849
## Lag 3e+05                      0.009121961
## Lag 4e+05                      0.013463720
## Lag 5e+05                     -0.004296466
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.029670803
## Lag 2e+05                      0.017779997
## Lag 3e+05                      0.028130999
## Lag 4e+05                      0.004441709
## Lag 5e+05                      0.003070608
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.29636608
## Lag 2e+05                       0.17537688
## Lag 3e+05                       0.09071157
## Lag 4e+05                       0.07080560
## Lag 5e+05                       0.05089448
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.27819095                 NaN
## Lag 2e+05                       0.12812944                 NaN
## Lag 3e+05                       0.09771414                 NaN
## Lag 4e+05                       0.05770162                 NaN
## Lag 5e+05                       0.05233042                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0             1.000000000         1.000000000                 NaN
## Lag 1e+05        -0.010262448         0.106634919                 NaN
## Lag 2e+05         0.001103472         0.049853057                 NaN
## Lag 3e+05        -0.006496813        -0.029428664                 NaN
## Lag 4e+05        -0.007063926         0.005664593                 NaN
## Lag 5e+05         0.010328523         0.047930052                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000         1.000000000          1.00000000
## Lag 1e+05        -0.006526363         0.012163730          0.23588593
## Lag 2e+05         0.004865949         0.003212733          0.10769224
## Lag 3e+05        -0.011715660         0.023986281          0.10674975
## Lag 4e+05         0.001235451         0.010046447          0.08018892
## Lag 5e+05        -0.009625251        -0.005246617          0.04146183
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0              1.0000000000            1.00000000           1.00000000
## Lag 1e+05          0.2211386103            0.22288160           0.31956481
## Lag 2e+05          0.0959221501            0.10365828           0.21757062
## Lag 3e+05          0.0630784988            0.06390551           0.13934087
## Lag 4e+05          0.0222374078            0.04979931           0.08966756
## Lag 5e+05          0.0006070651            0.01430520           0.08481844
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000                  NaN           1.00000000
## Lag 1e+05           0.20830556                  NaN           0.28689227
## Lag 2e+05           0.09839399                  NaN           0.15821494
## Lag 3e+05           0.07775237                  NaN           0.07549363
## Lag 4e+05           0.03555281                  NaN           0.07298278
## Lag 5e+05           0.02853138                  NaN           0.02763284
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000       1.00000000       1.00000000
## Lag 1e+05           0.15711614       0.20386911       0.10720211
## Lag 2e+05           0.07068030       0.09164690       0.03226088
## Lag 3e+05           0.04514573       0.07073131       0.03667348
## Lag 4e+05           0.03813517       0.06093778       0.01541817
## Lag 5e+05           0.04477554       0.02782266       0.01536491
## Chain 7 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                       1.00000000
## Lag 1e+05 0.17987958                       0.30418551
## Lag 2e+05 0.06616458                       0.17234295
## Lag 3e+05 0.04062654                       0.10317151
## Lag 4e+05 0.02603462                       0.05919729
## Lag 5e+05 0.01811805                       0.04419383
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.068624980
## Lag 2e+05                     -0.004403662
## Lag 3e+05                      0.008852522
## Lag 4e+05                     -0.022454676
## Lag 5e+05                      0.019557673
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.005980916
## Lag 2e+05                      0.003514619
## Lag 3e+05                      0.009728776
## Lag 4e+05                      0.004190767
## Lag 5e+05                     -0.037883866
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.29563161
## Lag 2e+05                       0.12540717
## Lag 3e+05                       0.09124414
## Lag 4e+05                       0.06317559
## Lag 5e+05                       0.01972196
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.27973077                 NaN
## Lag 2e+05                       0.13667845                 NaN
## Lag 3e+05                       0.05669814                 NaN
## Lag 4e+05                       0.06634228                 NaN
## Lag 5e+05                       0.02854105                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0             1.000000000         1.000000000                 NaN
## Lag 1e+05         0.002477492         0.099375890                 NaN
## Lag 2e+05        -0.001152193         0.028544575                 NaN
## Lag 3e+05        -0.010457892         0.020409249                 NaN
## Lag 4e+05        -0.015172438         0.034234793                 NaN
## Lag 5e+05        -0.017815328         0.005020669                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000        1.0000000000          1.00000000
## Lag 1e+05         0.053212406        0.0055569907          0.22946707
## Lag 2e+05         0.022321109        0.0150806564          0.08820948
## Lag 3e+05        -0.025293211       -0.0005585168          0.05291578
## Lag 4e+05         0.003421593       -0.0213551194          0.03893897
## Lag 5e+05         0.011371136        0.0104686809          0.02677868
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.23941875            0.24142672           0.31944545
## Lag 2e+05            0.12516811            0.10554649           0.20885308
## Lag 3e+05            0.07643474            0.04744658           0.14390528
## Lag 4e+05            0.02527472            0.04687684           0.08801910
## Lag 5e+05            0.03709380            0.01866032           0.05128301
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000                  NaN           1.00000000
## Lag 1e+05          0.198573201                  NaN           0.26761012
## Lag 2e+05          0.081632207                  NaN           0.12539122
## Lag 3e+05          0.040214750                  NaN           0.07585451
## Lag 4e+05          0.013625324                  NaN           0.05130556
## Lag 5e+05          0.004600815                  NaN           0.02661434
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000       1.00000000       1.00000000
## Lag 1e+05           0.17882194       0.20621276       0.11169881
## Lag 2e+05           0.08135737       0.08466386       0.02669493
## Lag 3e+05           0.03397391       0.06271596       0.02094325
## Lag 4e+05           0.02257184       0.04155520       0.02620520
## Lag 5e+05           0.03901024       0.02078500       0.02197635
## Chain 8 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.192226484                      0.305088334
## Lag 2e+05  0.096615876                      0.144467846
## Lag 3e+05  0.046707721                      0.085895073
## Lag 4e+05 -0.002569032                      0.040136388
## Lag 5e+05 -0.014328786                      0.009102839
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.043935021
## Lag 2e+05                     -0.002589349
## Lag 3e+05                      0.009026611
## Lag 4e+05                      0.001305383
## Lag 5e+05                      0.001052281
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.007984594
## Lag 2e+05                      0.008422647
## Lag 3e+05                     -0.012611203
## Lag 4e+05                     -0.012286644
## Lag 5e+05                     -0.028827041
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.27896124
## Lag 2e+05                       0.14878732
## Lag 3e+05                       0.07917982
## Lag 4e+05                       0.05885601
## Lag 5e+05                       0.01712633
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.25791066                 NaN
## Lag 2e+05                       0.11937180                 NaN
## Lag 3e+05                       0.06575066                 NaN
## Lag 4e+05                       0.04425693                 NaN
## Lag 5e+05                       0.05455539                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0             1.000000000         1.000000000                 NaN
## Lag 1e+05        -0.005083331         0.135371224                 NaN
## Lag 2e+05        -0.020299195         0.063081299                 NaN
## Lag 3e+05         0.012052156         0.012488064                 NaN
## Lag 4e+05        -0.036204151        -0.005652948                 NaN
## Lag 5e+05         0.004609006        -0.030604273                 NaN
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0            1.0000000000         1.000000000         1.000000000
## Lag 1e+05       -0.0001391938         0.016572586         0.233240346
## Lag 2e+05        0.0188088576         0.001763158         0.110101676
## Lag 3e+05       -0.0050238976         0.009389993         0.047180565
## Lag 4e+05        0.0034055021         0.012039326         0.006186067
## Lag 5e+05        0.0106706695        -0.002446455         0.000659803
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000           1.000000000           1.00000000
## Lag 1e+05            0.26293392           0.249300220           0.32926922
## Lag 2e+05            0.13702576           0.123348831           0.20895278
## Lag 3e+05            0.08836017           0.053256715           0.17321705
## Lag 4e+05            0.04151497           0.015581184           0.11576380
## Lag 5e+05            0.01515828          -0.006345339           0.07549173
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000                  NaN           1.00000000
## Lag 1e+05          0.180587497                  NaN           0.29708559
## Lag 2e+05          0.069157396                  NaN           0.16312855
## Lag 3e+05          0.039457266                  NaN           0.10947573
## Lag 4e+05          0.011311086                  NaN           0.08204621
## Lag 5e+05         -0.002471714                  NaN           0.03040432
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.168836613      0.239920368      0.103186778
## Lag 2e+05          0.093414241      0.118572091      0.033728852
## Lag 3e+05          0.037204513      0.062383002      0.009477534
## Lag 4e+05          0.002495196      0.008995628     -0.012701860
## Lag 5e+05         -0.024306741     -0.010361522     -0.028780925
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.58586                          0.04636 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -2.31370                          0.79421 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.74784                          1.39761 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                          0.77301 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                          0.02243                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                          0.09086                          0.29405 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                          0.58380                         -0.34330 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          1.70129                          2.25988 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                          0.14886                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          0.63245                         -0.07657 
##                 nodematch.region                 absdiff.sqrt.age 
##                          0.81347                         -0.27055 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.55796962                       0.96302137 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.02068445                       0.42707465 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.45455692                       0.16223115 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                       0.43951665 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                       0.98210470                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                       0.92760031                       0.76871683 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                       0.55935709                       0.73137105 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.08888791                       0.02382843 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.88166218                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.52709581                       0.93896726 
##                 nodematch.region                 absdiff.sqrt.age 
##                       0.41594899                       0.78674020 
## Joint P-value (lower = worse):  0.6264776 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          1.29628                          0.05266 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -1.41676                          3.06377 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.51303                          1.95912 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                         -0.15477 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                          4.16787                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                          1.14993                         -0.87783 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                          0.53778                          1.47790 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                         -0.45452                          0.51090 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                          0.40816                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                         -1.08848                          1.24367 
##                 nodematch.region                 absdiff.sqrt.age 
##                          1.66429                          0.91940 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                     1.948777e-01                     9.580023e-01 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                     1.565524e-01                     2.185653e-03 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                     6.079311e-01                     5.009839e-02 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                     8.770048e-01 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                     3.074603e-05                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                     2.501733e-01                     3.800372e-01 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                     5.907286e-01                     1.394340e-01 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                     6.494543e-01                     6.094201e-01 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                     6.831539e-01                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                     2.763834e-01                     2.136207e-01 
##                 nodematch.region                 absdiff.sqrt.age 
##                     9.605380e-02                     3.578838e-01 
## Joint P-value (lower = worse):  0.0002176157 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          -1.2454                          -0.8283 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           1.2592                          -0.7902 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           0.1869                          -3.6736 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                          -1.8569 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                           0.3341                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                           1.9363                          -0.8651 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                          -1.3613                          -1.4924 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          -0.3741                          -2.1222 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                           0.2405                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          -0.9402                          -0.9044 
##                 nodematch.region                 absdiff.sqrt.age 
##                          -1.9105                          -0.5343 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                     0.2129974437                     0.4075039322 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                     0.2079681963                     0.4293835414 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                     0.8517506252                     0.0002391221 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                     0.0633247033 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                     0.7382671986                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                     0.0528321748                     0.3869966955 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                     0.1734062164                     0.1355824612 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                     0.7083409440                     0.0338209916 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                     0.8099417678                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                     0.3471254349                     0.3657943591 
##                 nodematch.region                 absdiff.sqrt.age 
##                     0.0560686207                     0.5931341461 
## Joint P-value (lower = worse):  3.548353e-07 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -0.48936                         -0.21638 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.31582                         -0.79379 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.28546                         -1.11934 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                         -1.01205 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                         -1.09989                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                         -0.44359                          1.48919 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                         -0.47508                         -0.06266 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                         -1.20052                         -1.09855 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                          1.42565                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                         -0.40499                          0.15812 
##                 nodematch.region                 absdiff.sqrt.age 
##                         -0.65263                         -0.31392 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.6245891                        0.8286937 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.7521368                        0.4273179 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.7752926                        0.2629933 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                        0.3115165 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                        0.2713802                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                        0.6573412                        0.1364381 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                        0.6347316                        0.9500339 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                        0.2299389                        0.2719622 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                        0.1539700                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                        0.6854820                        0.8743584 
##                 nodematch.region                 absdiff.sqrt.age 
##                        0.5139954                        0.7535795 
## Joint P-value (lower = worse):  0.8460811 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          -1.0857                          -1.4754 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          -0.1683                          -1.8131 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          -0.1793                          -0.9198 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                           0.2109 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                          -0.1719                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                          -0.1663                          -0.9285 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                          -0.9722                           0.7286 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          -0.6054                          -0.5174 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                          -0.9202                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                           0.4782                          -1.3780 
##                 nodematch.region                 absdiff.sqrt.age 
##                          -0.6634                          -1.1113 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.27761840                       0.14009648 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.86633529                       0.06981048 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.85767044                       0.35766747 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                       0.83292718 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                       0.86355493                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                       0.86790476                       0.35316076 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                       0.33093618                       0.46621988 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.54490012                       0.60489939 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.35746433                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.63251446                       0.16819204 
##                 nodematch.region                 absdiff.sqrt.age 
##                       0.50706633                       0.26643093 
## Joint P-value (lower = worse):  0.9736067 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -0.17484                          0.06719 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.49540                          0.58297 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.84212                         -0.56957 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                          0.29477 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                         -1.41999                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                         -1.43625                         -0.03870 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                          0.20802                          0.35902 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          0.07009                          1.57137 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                         -0.37015                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                         -0.30350                         -0.40011 
##                 nodematch.region                 absdiff.sqrt.age 
##                         -0.36663                         -0.57050 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.8612090                        0.9464303 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.6203151                        0.5599110 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.3997208                        0.5689718 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                        0.7681717 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                        0.1556095                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                        0.1509306                        0.9691257 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                        0.8352112                        0.7195835 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                        0.9441259                        0.1160971 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                        0.7112678                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                        0.7615088                        0.6890774 
##                 nodematch.region                 absdiff.sqrt.age 
##                        0.7138941                        0.5683407 
## Joint P-value (lower = worse):  0.9659163 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           1.8164                           1.1045 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           0.9034                           0.2677 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           1.5342                          -0.3186 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                           0.6488 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                           2.9017                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                           0.5894                           0.1196 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                           1.0811                           0.5606 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                           0.3993                           1.1820 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                           1.9794                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                           0.2143                           1.5214 
##                 nodematch.region                 absdiff.sqrt.age 
##                           1.4462                           2.3482 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                      0.069314366                      0.269396181 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                      0.366311842                      0.788945019 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                      0.124987596                      0.750053545 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                      0.516452893 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                      0.003711976                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                      0.555595983                      0.904801619 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                      0.279634887                      0.575064616 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                      0.689693612                      0.237205411 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                      0.047770375                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                      0.830313226                      0.128167887 
##                 nodematch.region                 absdiff.sqrt.age 
##                      0.148131849                      0.018866778 
## Joint P-value (lower = worse):  0.227469 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -1.53292                         -2.43825 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          0.37620                         -1.15875 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -1.51114                         -1.22836 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                         -1.73211 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                          0.21532                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                         -0.17301                         -1.25302 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                         -1.49534                         -1.25172 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                         -0.45148                         -2.05302 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                         -0.26572                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          0.07179                         -1.02375 
##                 nodematch.region                 absdiff.sqrt.age 
##                         -1.13080                          0.49391 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.12529533                       0.01475852 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.70676651                       0.24655811 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.13075392                       0.21931096 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                       0.08325448 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                       0.82951859                              NaN 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                       0.86264172                       0.21019958 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                       0.13482572                       0.21067274 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.65164658                       0.04007052 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.79045401                              NaN 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.94277115                       0.30595257 
##                 nodematch.region                 absdiff.sqrt.age 
##                       0.25813744                       0.62137259 
## Joint P-value (lower = worse):  0.1353675 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Summary of model fit

Model 1

summary(est.i.buildup.unbal[[1]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55909a01c678>
## 
## Iterations:  2 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC % p-value    
## edges                  -11.48697    0.04574      0  <1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 2

summary(est.i.buildup.unbal[[2]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x5590b53638c0>
## 
## Iterations:  2 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC % p-value    
## edges                  -11.52227    0.05491      0  <1e-04 ***
## nodefactor.race..wa.B    0.27119    0.12122      0  0.0253 *  
## nodefactor.race..wa.H   -0.01052    0.10485      0  0.9201    
## nodematch.role.class.I      -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 3

summary(est.i.buildup.unbal[[3]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa", 
##     diff = TRUE) + offset(nodematch("role.class", diff = TRUE, 
##     keep = 1:2))
## <environment: 0x5590cb18de58>
## 
## Iterations:  15 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC % p-value    
## edges                      9.991  36671.497    100       1    
## nodefactor.race..wa.B    -20.966  36671.497    100       1    
## nodefactor.race..wa.H    -21.417  36671.497    100       1    
## nodematch.race..wa.B     -22.283         NA     NA      NA    
## nodematch.race..wa.H      21.647  36671.497    100       1    
## nodematch.race..wa.O     -21.599  36671.497    100       1    
## nodematch.role.class.I      -Inf      0.000      0  <1e-04 ***
## nodematch.role.class.R      -Inf      0.000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 4

summary(est.i.buildup.unbal[[4]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa", 
##     base = 3) + nodematch("race..wa", diff = TRUE) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x5590e73c7b10>
## 
## Iterations:  15 out of 400 
## 
## Monte Carlo MLE Results:
##                                    Estimate Std. Error MCMC % p-value    
## edges                             8.438e+01  4.162e+04    100   0.998    
## nodefactor.deg.main.deg.pers.0.1  8.903e-01  9.087e-02      0  <1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -6.957e-01  1.703e-01      0  <1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.195e+00  1.733e-01      0  <1e-04 ***
## nodefactor.deg.main.deg.pers.1.1  8.629e-01  9.885e-02      0  <1e-04 ***
## nodefactor.deg.main.deg.pers.1.2  7.764e-01  9.574e-02      0  <1e-04 ***
## nodefactor.race..wa.B            -9.525e+01  4.162e+04    100   0.998    
## nodefactor.race..wa.H            -9.564e+01  4.162e+04    100   0.998    
## nodematch.race..wa.B              8.203e+01         NA     NA      NA    
## nodematch.race..wa.H              9.589e+01  4.162e+04    100   0.998    
## nodematch.race..wa.O             -9.584e+01  4.162e+04    100   0.998    
## nodematch.role.class.I                 -Inf  0.000e+00      0  <1e-04 ***
## nodematch.role.class.R                 -Inf  0.000e+00      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 5

summary(est.i.buildup.unbal[[5]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + offset(nodematch("role.class", diff = TRUE, 
##     keep = 1:2))
## <environment: 0x5591038b5650>
## 
## Iterations:  14 out of 400 
## 
## Monte Carlo MLE Results:
##                                    Estimate Std. Error MCMC % p-value    
## edges                             101.56848 6757.78583    100  0.9880    
## nodefactor.deg.main.deg.pers.0.1    0.88905    0.09102      0  <1e-04 ***
## nodefactor.deg.main.deg.pers.0.2   -0.70233    0.17183      0  <1e-04 ***
## nodefactor.deg.main.deg.pers.1.0   -2.22996    0.17173      0  <1e-04 ***
## nodefactor.deg.main.deg.pers.1.1    0.81599    0.09922      0  <1e-04 ***
## nodefactor.deg.main.deg.pers.1.2    0.75931    0.09639      0  <1e-04 ***
## nodefactor.race..wa.B            -112.14700 6757.78583    100  0.9868    
## nodefactor.race..wa.H            -112.47556 6757.78583    100  0.9867    
## nodefactor.region.EW               -0.28984    0.11751      0  0.0136 *  
## nodefactor.region.OW               -0.43370    0.07578      0  <1e-04 ***
## nodematch.race..wa.B               98.34341         NA     NA      NA    
## nodematch.race..wa.H              112.73996 6757.78584    100  0.9867    
## nodematch.race..wa.O             -112.69145 6757.78583    100  0.9867    
## nodematch.role.class.I                 -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.R                 -Inf    0.00000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 6

summary(est.i.buildup.unbal[[6]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + absdiff("sqrt.age") + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55911fec3d80>
## 
## Iterations:  15 out of 400 
## 
## Monte Carlo MLE Results:
##                                    Estimate Std. Error MCMC % p-value    
## edges                              90.60542         NA     NA      NA    
## nodefactor.deg.main.deg.pers.0.1    0.89118    0.09099      0  <1e-04 ***
## nodefactor.deg.main.deg.pers.0.2   -0.69817    0.17071      0  <1e-04 ***
## nodefactor.deg.main.deg.pers.1.0   -2.22984    0.17398      0  <1e-04 ***
## nodefactor.deg.main.deg.pers.1.1    0.82661    0.09956      0  <1e-04 ***
## nodefactor.deg.main.deg.pers.1.2    0.75442    0.09676      0  <1e-04 ***
## nodefactor.race..wa.B            -100.60096         NA     NA      NA    
## nodefactor.race..wa.H            -100.92762         NA     NA      NA    
## nodefactor.region.EW               -0.28696    0.11653      0  0.0138 *  
## nodefactor.region.OW               -0.43448    0.07618      0  <1e-04 ***
## nodematch.race..wa.B               83.09412         NA     NA      NA    
## nodematch.race..wa.H              101.19042         NA     NA      NA    
## nodematch.race..wa.O             -101.14353         NA     NA      NA    
## absdiff.sqrt.age                   -0.61070    0.06841      0  <1e-04 ***
## nodematch.role.class.I                 -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.R                 -Inf    0.00000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 7

summary(est.i.buildup.unbal[[7]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("riskg") + 
##     nodefactor("race..wa", base = 3) + nodefactor("region", base = 2) + 
##     nodematch("race..wa", diff = TRUE) + absdiff("sqrt.age") + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55913c6c49c8>
## 
## Iterations:  31 out of 400 
## 
## Monte Carlo MLE Results:
##                                    Estimate Std. Error MCMC %  p-value    
## edges                            -2.278e+01  3.860e+04    100 0.999529    
## nodefactor.deg.main.deg.pers.0.1  9.011e-01  9.121e-02      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -6.117e-01  1.701e-01      0 0.000324 ***
## nodefactor.deg.main.deg.pers.1.0 -2.173e+00  1.727e-01      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1  9.129e-01  9.967e-02      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2  6.626e-01  9.634e-02      0  < 1e-04 ***
## nodefactor.riskg.O2               8.895e-02         NA     NA       NA    
## nodefactor.riskg.O3               1.668e+01         NA     NA       NA    
## nodefactor.riskg.O4               1.965e+01         NA     NA       NA    
## nodefactor.riskg.Y1               3.512e+00         NA     NA       NA    
## nodefactor.riskg.Y2               1.550e+01         NA     NA       NA    
## nodefactor.riskg.Y3               1.780e+01         NA     NA       NA    
## nodefactor.riskg.Y4               2.019e+01         NA     NA       NA    
## nodefactor.race..wa.B            -2.501e+01         NA     NA       NA    
## nodefactor.race..wa.H            -2.527e+01         NA     NA       NA    
## nodefactor.region.EW             -1.512e-01  1.179e-01      0 0.199813    
## nodefactor.region.OW             -3.325e-01  7.602e-02      0  < 1e-04 ***
## nodematch.race..wa.B             -2.158e+01         NA     NA       NA    
## nodematch.race..wa.H              2.549e+01         NA     NA       NA    
## nodematch.race..wa.O             -2.556e+01         NA     NA       NA    
## absdiff.sqrt.age                 -5.514e-01  7.195e-02      0  < 1e-04 ***
## nodematch.role.class.I                 -Inf  0.000e+00      0  < 1e-04 ***
## nodematch.role.class.R                 -Inf  0.000e+00      0  < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 8

summary(est.i.buildup.unbal[[8]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("riskg") + 
##     nodefactor("race..wa", base = 3) + nodefactor("region", base = 2) + 
##     nodematch("race..wa", diff = TRUE) + nodematch("region", 
##     diff = FALSE) + absdiff("sqrt.age") + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x559159232258>
## 
## Iterations:  29 out of 400 
## 
## Monte Carlo MLE Results:
##                                    Estimate Std. Error MCMC %  p-value    
## edges                            -2.752e+01         NA     NA       NA    
## nodefactor.deg.main.deg.pers.0.1  9.008e-01  9.101e-02      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -6.115e-01  1.710e-01      0 0.000349 ***
## nodefactor.deg.main.deg.pers.1.0 -2.173e+00  1.725e-01      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1  9.130e-01  9.909e-02      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2  6.629e-01  9.625e-02      0  < 1e-04 ***
## nodefactor.riskg.O2              -1.975e+00         NA     NA       NA    
## nodefactor.riskg.O3               1.616e+01  2.255e+04    100 0.999428    
## nodefactor.riskg.O4               1.913e+01  1.841e+04    100 0.999171    
## nodefactor.riskg.Y1               2.439e+00         NA     NA       NA    
## nodefactor.riskg.Y2               1.497e+01  2.377e+04    100 0.999497    
## nodefactor.riskg.Y3               1.728e+01  1.681e+04    100 0.999180    
## nodefactor.riskg.Y4               1.967e+01  1.989e+04    100 0.999211    
## nodefactor.race..wa.B            -2.074e+01         NA     NA       NA    
## nodefactor.race..wa.H            -2.099e+01         NA     NA       NA    
## nodefactor.region.EW              5.909e-01  1.021e-01      0  < 1e-04 ***
## nodefactor.region.OW              4.858e-02  6.053e-02      0 0.422238    
## nodematch.race..wa.B             -1.766e+00         NA     NA       NA    
## nodematch.race..wa.H              2.114e+01         NA     NA       NA    
## nodematch.race..wa.O             -2.128e+01         NA     NA       NA    
## nodematch.region                  1.787e+00  1.219e-01      0  < 1e-04 ***
## absdiff.sqrt.age                 -5.527e-01  7.182e-02      0  < 1e-04 ***
## nodematch.role.class.I                 -Inf  0.000e+00      0  < 1e-04 ***
## nodematch.role.class.R                 -Inf  0.000e+00      0  < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Network diagnostics

Model 1

(dx_inst1 <- netdx(est.i.buildup.unbal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.unbal[[8]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            478.512  478.151   -0.001 21.588
## nodefactor.deg.main.deg.pers.0.1      NA   66.628       NA  8.428
## nodefactor.deg.main.deg.pers.0.2      NA   69.894       NA  8.629
## nodefactor.deg.main.deg.pers.1.0      NA  310.384       NA 20.104
## nodefactor.deg.main.deg.pers.1.1      NA   54.334       NA  7.526
## nodefactor.deg.main.deg.pers.1.2      NA   63.214       NA  8.140
## nodefactor.riskg.O2                   NA   55.267       NA  7.636
## nodefactor.riskg.O3                   NA   54.681       NA  7.615
## nodefactor.riskg.O4                   NA   54.918       NA  7.607
## nodefactor.riskg.Y1                   NA  183.458       NA 14.666
## nodefactor.riskg.Y2                   NA  184.473       NA 14.793
## nodefactor.riskg.Y3                   NA  184.753       NA 14.880
## nodefactor.riskg.Y4                   NA  184.013       NA 14.713
## nodefactor.race..wa.B                 NA   58.279       NA  7.874
## nodefactor.race..wa.H                 NA  103.653       NA 10.693
## nodefactor.region.EW                  NA   96.562       NA 10.377
## nodefactor.region.OW                  NA  313.897       NA 20.258
## nodematch.race..wa.B                  NA    1.777       NA  1.314
## nodematch.race..wa.H                  NA    5.607       NA  2.370
## nodematch.race..wa.O                  NA  329.927       NA 17.978
## nodematch.region                      NA  212.223       NA 14.458
## absdiff.sqrt.age                      NA  544.622       NA 30.164
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst1, type="formation")

plot(dx_inst1, type="duration")

plot(dx_inst1, type="dissolution")

Model 2

(dx_inst2 <- netdx(est.i.buildup.unbal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.unbal[[8]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            478.512  478.600    0.000 21.803
## nodefactor.deg.main.deg.pers.0.1      NA   67.093       NA  8.477
## nodefactor.deg.main.deg.pers.0.2      NA   70.228       NA  8.640
## nodefactor.deg.main.deg.pers.1.0      NA  309.292       NA 20.177
## nodefactor.deg.main.deg.pers.1.1      NA   54.026       NA  7.481
## nodefactor.deg.main.deg.pers.1.2      NA   63.229       NA  8.268
## nodefactor.riskg.O2                   NA   55.433       NA  7.675
## nodefactor.riskg.O3                   NA   54.887       NA  7.532
## nodefactor.riskg.O4                   NA   54.832       NA  7.600
## nodefactor.riskg.Y1                   NA  183.662       NA 14.836
## nodefactor.riskg.Y2                   NA  184.776       NA 14.915
## nodefactor.riskg.Y3                   NA  184.370       NA 14.859
## nodefactor.riskg.Y4                   NA  184.497       NA 14.793
## nodefactor.race..wa.B             75.186   75.077   -0.001  8.988
## nodefactor.race..wa.H            100.835  100.951    0.001 10.615
## nodefactor.region.EW                  NA   95.490       NA 10.341
## nodefactor.region.OW                  NA  313.575       NA 20.398
## nodematch.race..wa.B                  NA    2.929       NA  1.709
## nodematch.race..wa.H                  NA    5.316       NA  2.306
## nodematch.race..wa.O                  NA  318.696       NA 17.679
## nodematch.region                      NA  212.870       NA 14.501
## absdiff.sqrt.age                      NA  545.496       NA 30.208
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst2, type="formation")

plot(dx_inst2, type="duration")

plot(dx_inst2, type="dissolution")

Model 3

(dx_inst3 <- netdx(est.i.buildup.unbal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.unbal[[8]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            478.512  472.171   -0.013 22.093
## nodefactor.deg.main.deg.pers.0.1      NA   66.144       NA  8.421
## nodefactor.deg.main.deg.pers.0.2      NA   69.451       NA  8.658
## nodefactor.deg.main.deg.pers.1.0      NA  305.117       NA 20.556
## nodefactor.deg.main.deg.pers.1.1      NA   53.075       NA  7.424
## nodefactor.deg.main.deg.pers.1.2      NA   62.183       NA  8.121
## nodefactor.riskg.O2                   NA   54.728       NA  7.632
## nodefactor.riskg.O3                   NA   54.288       NA  7.578
## nodefactor.riskg.O4                   NA   54.204       NA  7.687
## nodefactor.riskg.Y1                   NA  180.928       NA 14.849
## nodefactor.riskg.Y2                   NA  182.341       NA 14.800
## nodefactor.riskg.Y3                   NA  181.632       NA 14.656
## nodefactor.riskg.Y4                   NA  182.240       NA 14.769
## nodefactor.race..wa.B             75.186   80.698    0.073  9.033
## nodefactor.race..wa.H            100.835  106.476    0.056 11.171
## nodefactor.region.EW                  NA   94.642       NA 10.229
## nodefactor.region.OW                  NA  308.361       NA 20.443
## nodematch.race..wa.B               2.538    0.000   -1.000  0.000
## nodematch.race..wa.H              13.275    7.484   -0.436  2.754
## nodematch.race..wa.O             286.884  292.482    0.020 17.165
## nodematch.region                      NA  210.233       NA 14.609
## absdiff.sqrt.age                      NA  538.550       NA 30.700
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst3, type="formation")

plot(dx_inst3, type="duration")

plot(dx_inst3, type="dissolution")

Model 4

(dx_inst4 <- netdx(est.i.buildup.unbal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.unbal[[8]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            478.512  457.358   -0.044 22.510
## nodefactor.deg.main.deg.pers.0.1 173.175  161.054   -0.070 14.391
## nodefactor.deg.main.deg.pers.0.2  37.217   37.117   -0.003  6.259
## nodefactor.deg.main.deg.pers.1.0  36.568   36.591    0.001  6.064
## nodefactor.deg.main.deg.pers.1.1 135.364  126.785   -0.063 12.634
## nodefactor.deg.main.deg.pers.1.2 145.870  138.280   -0.052 12.989
## nodefactor.riskg.O2                   NA   51.199       NA  7.405
## nodefactor.riskg.O3                   NA   52.176       NA  7.526
## nodefactor.riskg.O4                   NA   53.042       NA  7.566
## nodefactor.riskg.Y1                   NA  170.601       NA 14.429
## nodefactor.riskg.Y2                   NA  174.565       NA 14.776
## nodefactor.riskg.Y3                   NA  178.795       NA 14.709
## nodefactor.riskg.Y4                   NA  179.387       NA 14.726
## nodefactor.race..wa.B             75.186   75.418    0.003  8.972
## nodefactor.race..wa.H            100.835  102.511    0.017 10.970
## nodefactor.region.EW                  NA   91.995       NA 10.231
## nodefactor.region.OW                  NA  309.464       NA 20.847
## nodematch.race..wa.B               2.538    0.000   -1.000  0.000
## nodematch.race..wa.H              13.275    7.025   -0.471  2.651
## nodematch.race..wa.O             286.884  286.454   -0.001 17.233
## nodematch.region                      NA  200.974       NA 14.573
## absdiff.sqrt.age                      NA  522.794       NA 31.006
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst4, type="formation")

plot(dx_inst4, type="duration")

plot(dx_inst4, type="dissolution")

Model 5

(dx_inst5 <- netdx(est.i.buildup.unbal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.unbal[[8]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            478.512  454.340   -0.051 22.602
## nodefactor.deg.main.deg.pers.0.1 173.175  159.186   -0.081 14.583
## nodefactor.deg.main.deg.pers.0.2  37.217   37.268    0.001  6.165
## nodefactor.deg.main.deg.pers.1.0  36.568   36.634    0.002  6.115
## nodefactor.deg.main.deg.pers.1.1 135.364  125.559   -0.072 12.475
## nodefactor.deg.main.deg.pers.1.2 145.870  136.589   -0.064 13.170
## nodefactor.riskg.O2                   NA   50.986       NA  7.357
## nodefactor.riskg.O3                   NA   51.654       NA  7.376
## nodefactor.riskg.O4                   NA   53.332       NA  7.533
## nodefactor.riskg.Y1                   NA  167.251       NA 14.365
## nodefactor.riskg.Y2                   NA  173.222       NA 14.631
## nodefactor.riskg.Y3                   NA  178.388       NA 14.994
## nodefactor.riskg.Y4                   NA  179.833       NA 14.826
## nodefactor.race..wa.B             75.186   74.682   -0.007  8.862
## nodefactor.race..wa.H            100.835  101.613    0.008 10.964
## nodefactor.region.EW              83.389   81.191   -0.026  9.453
## nodefactor.region.OW             242.159  237.964   -0.017 17.266
## nodematch.race..wa.B               2.538    0.000   -1.000  0.000
## nodematch.race..wa.H              13.275    6.960   -0.476  2.626
## nodematch.race..wa.O             286.884  285.004   -0.007 17.318
## nodematch.region                      NA  225.097       NA 15.815
## absdiff.sqrt.age                      NA  519.067       NA 31.054
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst5, type="formation")

plot(dx_inst5, type="duration")

plot(dx_inst5, type="dissolution")

Model 6

(dx_inst6 <- netdx(est.i.buildup.unbal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.unbal[[8]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            478.512  446.085   -0.068 22.951
## nodefactor.deg.main.deg.pers.0.1 173.175  154.292   -0.109 14.452
## nodefactor.deg.main.deg.pers.0.2  37.217   37.210    0.000  6.218
## nodefactor.deg.main.deg.pers.1.0  36.568   36.481   -0.002  6.163
## nodefactor.deg.main.deg.pers.1.1 135.364  121.952   -0.099 12.463
## nodefactor.deg.main.deg.pers.1.2 145.870  132.945   -0.089 13.183
## nodefactor.riskg.O2                   NA   46.306       NA  7.204
## nodefactor.riskg.O3                   NA   47.113       NA  7.235
## nodefactor.riskg.O4                   NA   48.703       NA  7.450
## nodefactor.riskg.Y1                   NA  167.969       NA 14.447
## nodefactor.riskg.Y2                   NA  173.826       NA 14.982
## nodefactor.riskg.Y3                   NA  178.506       NA 14.976
## nodefactor.riskg.Y4                   NA  180.414       NA 15.312
## nodefactor.race..wa.B             75.186   72.377   -0.037  8.903
## nodefactor.race..wa.H            100.835   99.909   -0.009 10.949
## nodefactor.region.EW              83.389   80.133   -0.039  9.417
## nodefactor.region.OW             242.159  235.312   -0.028 17.628
## nodematch.race..wa.B               2.538    0.000   -1.000  0.000
## nodematch.race..wa.H              13.275    6.843   -0.484  2.631
## nodematch.race..wa.O             286.884  280.643   -0.022 17.333
## nodematch.region                      NA  220.165       NA 15.824
## absdiff.sqrt.age                 379.987  367.744   -0.032 22.836
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst6, type="formation")

plot(dx_inst6, type="duration")

plot(dx_inst6, type="dissolution")

Model 7

(dx_inst7 <- netdx(est.i.buildup.unbal[[7]], nsims = 10, nsteps = 1000, ncores = 4))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            478.512  245.389   -0.487 33.031
## nodefactor.deg.main.deg.pers.0.1 173.175   67.775   -0.609 13.006
## nodefactor.deg.main.deg.pers.0.2  37.217   27.004   -0.274  5.924
## nodefactor.deg.main.deg.pers.1.0  36.568   35.360   -0.033  6.133
## nodefactor.deg.main.deg.pers.1.1 135.364   53.190   -0.607 10.671
## nodefactor.deg.main.deg.pers.1.2 145.870   63.025   -0.568 12.096
## nodefactor.riskg.O2                0.401    0.000   -1.000  0.000
## nodefactor.riskg.O3                6.856    5.344   -0.221  2.330
## nodefactor.riskg.O4              109.513   67.684   -0.382 11.784
## nodefactor.riskg.Y1                1.349    0.000   -1.000  0.000
## nodefactor.riskg.Y2                8.202    6.746   -0.178  2.621
## nodefactor.riskg.Y3               70.786   64.750   -0.085  8.605
## nodefactor.riskg.Y4              762.012  346.255   -0.546 54.176
## nodefactor.race..wa.B             75.186   35.660   -0.526  7.612
## nodefactor.race..wa.H            100.835   52.334   -0.481  9.910
## nodefactor.region.EW              83.389   45.145   -0.459  8.778
## nodefactor.region.OW             242.159  139.210   -0.425 20.481
## nodematch.race..wa.B               2.538    0.000   -1.000  0.000
## nodematch.race..wa.H              13.275    3.174   -0.761  1.837
## nodematch.race..wa.O             286.884  160.569   -0.440 22.279
## absdiff.sqrt.age                 379.987  222.382   -0.415 29.943
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst7, type="formation")

plot(dx_inst7, type="duration")

plot(dx_inst7, type="dissolution")

Model 8

(dx_inst8 <- netdx(est.i.buildup.unbal[[7]], nsims = 10, nsteps = 1000, ncores = 4))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            478.512  245.723   -0.486 32.751
## nodefactor.deg.main.deg.pers.0.1 173.175   67.699   -0.609 12.910
## nodefactor.deg.main.deg.pers.0.2  37.217   27.002   -0.274  5.907
## nodefactor.deg.main.deg.pers.1.0  36.568   35.325   -0.034  6.057
## nodefactor.deg.main.deg.pers.1.1 135.364   53.148   -0.607 10.490
## nodefactor.deg.main.deg.pers.1.2 145.870   63.154   -0.567 12.022
## nodefactor.riskg.O2                0.401    0.000   -1.000  0.000
## nodefactor.riskg.O3                6.856    5.368   -0.217  2.355
## nodefactor.riskg.O4              109.513   67.841   -0.381 11.793
## nodefactor.riskg.Y1                1.349    0.000   -1.000  0.000
## nodefactor.riskg.Y2                8.202    6.817   -0.169  2.616
## nodefactor.riskg.Y3               70.786   64.652   -0.087  8.615
## nodefactor.riskg.Y4              762.012  346.767   -0.545 53.571
## nodefactor.race..wa.B             75.186   35.785   -0.524  7.613
## nodefactor.race..wa.H            100.835   52.678   -0.478  9.934
## nodefactor.region.EW              83.389   45.352   -0.456  8.683
## nodefactor.region.OW             242.159  139.693   -0.423 20.327
## nodematch.race..wa.B               2.538    0.000   -1.000  0.000
## nodematch.race..wa.H              13.275    3.159   -0.762  1.833
## nodematch.race..wa.O             286.884  160.420   -0.441 22.136
## absdiff.sqrt.age                 379.987  222.718   -0.414 29.684
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst8, type="formation")

plot(dx_inst8, type="duration")

plot(dx_inst8, type="dissolution")